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You searched for subject:(Tensor factorization). Showing records 1 – 24 of 24 total matches.

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University of Houston

1. Ng, Wai Hin 1987-. Tensor Products and Factorizations of Operator Systems.

Degree: Mathematics, Department of, 2016, University of Houston

 In this dissertation, we start by studying the operator system maximal tensor product, called max, in [17] from different perspectives. One approach is by the… (more)

Subjects/Keywords: Tensor products; Factorization

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APA (6th Edition):

Ng, W. H. 1. (2016). Tensor Products and Factorizations of Operator Systems. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ng, Wai Hin 1987-. “Tensor Products and Factorizations of Operator Systems.” 2016. Thesis, University of Houston. Accessed April 26, 2019. http://hdl.handle.net/10657/3186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ng, Wai Hin 1987-. “Tensor Products and Factorizations of Operator Systems.” 2016. Web. 26 Apr 2019.

Vancouver:

Ng WH1. Tensor Products and Factorizations of Operator Systems. [Internet] [Thesis]. University of Houston; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10657/3186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ng WH1. Tensor Products and Factorizations of Operator Systems. [Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/3186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Phan, Anh Huy. Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用.

Degree: 博士(工学), 2017, Kyushu Institute of Technology / 九州工業大学

九州工業大学博士学位論文 学位記番号:生工博乙第8号 学位授与年月日:平成23年9月30日

1 Introduction|2 Alternating Least Squares Algorithm and Its Variations|3 Appropriate ALS Algorithms for NTF and NTD|4 All-at-Once Algorithms for Tensor Decompositions|5 Large-Scale Tensor Factorization|6 Simulations and Results|7 Applications for Feature Extraction and Classification|8 Conclusions

平成23年度

Advisors/Committee Members: 松岡, 清利.

Subjects/Keywords: Tensor Decomposition; nonnegative tensor factorization; Gauss-Newton; large-scale tensor factorization; Alternative least Squares(ALS); feature extraction

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APA (6th Edition):

Phan, A. H. (2017). Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用. (Thesis). Kyushu Institute of Technology / 九州工業大学. Retrieved from http://hdl.handle.net/10228/4896

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Phan, Anh Huy. “Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用.” 2017. Thesis, Kyushu Institute of Technology / 九州工業大学. Accessed April 26, 2019. http://hdl.handle.net/10228/4896.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Phan, Anh Huy. “Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用.” 2017. Web. 26 Apr 2019.

Vancouver:

Phan AH. Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用. [Internet] [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10228/4896.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Phan AH. Algorithms for Tensor Decompositions and Applications : テンソル分解のためのアルゴリズムとその応用. [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. Available from: http://hdl.handle.net/10228/4896

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

3. Banerjee, Anjishnu. Scalable Nonparametric Bayes Learning .

Degree: 2013, Duke University

  Capturing high dimensional complex ensembles of data is becoming commonplace in a variety of application areas. Some examples include biological studies exploring relationships between… (more)

Subjects/Keywords: Statistics; Bayes; Gaussian process; high-dimensional; nonparametric; random projections; tensor factorization

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APA (6th Edition):

Banerjee, A. (2013). Scalable Nonparametric Bayes Learning . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/7177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Banerjee, Anjishnu. “Scalable Nonparametric Bayes Learning .” 2013. Thesis, Duke University. Accessed April 26, 2019. http://hdl.handle.net/10161/7177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Banerjee, Anjishnu. “Scalable Nonparametric Bayes Learning .” 2013. Web. 26 Apr 2019.

Vancouver:

Banerjee A. Scalable Nonparametric Bayes Learning . [Internet] [Thesis]. Duke University; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10161/7177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Banerjee A. Scalable Nonparametric Bayes Learning . [Thesis]. Duke University; 2013. Available from: http://hdl.handle.net/10161/7177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

4. -3562-6540. Learning and validating clinically meaningful phenotypes from electronic health data.

Degree: Computational Science, Engineering, and Mathematics, 2018, University of Texas – Austin

 The ever-growing adoption of electronic health records (EHR) to record patients' health journeys has resulted in vast amounts of heterogeneous, complex, and unwieldy information [Hripcsak… (more)

Subjects/Keywords: Computational phenotyping; Tensor factorization; Machine learning; Medical informatics; Model output validation

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APA (6th Edition):

-3562-6540. (2018). Learning and validating clinically meaningful phenotypes from electronic health data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/69179

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3562-6540. “Learning and validating clinically meaningful phenotypes from electronic health data.” 2018. Thesis, University of Texas – Austin. Accessed April 26, 2019. http://hdl.handle.net/2152/69179.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-3562-6540. “Learning and validating clinically meaningful phenotypes from electronic health data.” 2018. Web. 26 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3562-6540. Learning and validating clinically meaningful phenotypes from electronic health data. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2152/69179.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3562-6540. Learning and validating clinically meaningful phenotypes from electronic health data. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/69179

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Western Ontario

5. Feng, Boyu. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.

Degree: 2018, University of Western Ontario

 The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on… (more)

Subjects/Keywords: Nonnegative matrix factorization; nonnegative tensor factorization; hyperspectral image; spectral unmixing; dimension reduction; Geographic Information Sciences; Remote Sensing

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APA (6th Edition):

Feng, B. (2018). Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/5732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Feng, Boyu. “Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.” 2018. Thesis, University of Western Ontario. Accessed April 26, 2019. https://ir.lib.uwo.ca/etd/5732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Feng, Boyu. “Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.” 2018. Web. 26 Apr 2019.

Vancouver:

Feng B. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. [Internet] [Thesis]. University of Western Ontario; 2018. [cited 2019 Apr 26]. Available from: https://ir.lib.uwo.ca/etd/5732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Feng B. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. [Thesis]. University of Western Ontario; 2018. Available from: https://ir.lib.uwo.ca/etd/5732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universiteit Utrecht

6. Steeg, F. van. Context-aware recommender systems.

Degree: 2015, Universiteit Utrecht

 Recommender systems try to predict users' preferences for certain items, given a set of historical data. Multiple different techniques are available that make these systems… (more)

Subjects/Keywords: recommender; recommendations; matrix; tensor; factorization; decomposition; svd; context; time; candecomp; parafac; implicit; music

…neighborhood method is combined with matrix factorization (Chapter 3) to increase prediction accuracy… …matrices is very expensive. More recently, matrix factorization techniques based on SVD have been… …the user likes the corre24 U×I A U×F ≈ X × F×I YT Figure 2.7: Matrix factorization… …overfitting of the sparse training data, matrix factorization techniques need to regularize the… …model. Chapter 3 will further explore the details of PCA, SVD and SVD-like factorization… 

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APA (6th Edition):

Steeg, F. v. (2015). Context-aware recommender systems. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/319980

Chicago Manual of Style (16th Edition):

Steeg, F van. “Context-aware recommender systems.” 2015. Masters Thesis, Universiteit Utrecht. Accessed April 26, 2019. http://dspace.library.uu.nl:8080/handle/1874/319980.

MLA Handbook (7th Edition):

Steeg, F van. “Context-aware recommender systems.” 2015. Web. 26 Apr 2019.

Vancouver:

Steeg Fv. Context-aware recommender systems. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2019 Apr 26]. Available from: http://dspace.library.uu.nl:8080/handle/1874/319980.

Council of Science Editors:

Steeg Fv. Context-aware recommender systems. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/319980

7. Kodewitz, Andreas. Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan".

Degree: Docteur es, Mathématiques appliquées, 2013, Evry-Val d'Essonne

Dans cette thèse, nous explorons de nouvelles méthodes d’analyse d’images pour la détection précoce des changements métaboliques cérébraux causés par la maladie d’Alzheimer. Nous introduisons… (more)

Subjects/Keywords: Factorisation tensorielle non-négative; Non-negative tensor factorization; Importance map; Alzheimer's disease

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APA (6th Edition):

Kodewitz, A. (2013). Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan". (Doctoral Dissertation). Evry-Val d'Essonne. Retrieved from http://www.theses.fr/2013EVRY0005

Chicago Manual of Style (16th Edition):

Kodewitz, Andreas. “Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan".” 2013. Doctoral Dissertation, Evry-Val d'Essonne. Accessed April 26, 2019. http://www.theses.fr/2013EVRY0005.

MLA Handbook (7th Edition):

Kodewitz, Andreas. “Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan".” 2013. Web. 26 Apr 2019.

Vancouver:

Kodewitz A. Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan". [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; 2013. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2013EVRY0005.

Council of Science Editors:

Kodewitz A. Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans : Méthode d'analyse de grands volumes de données : appliquées à la détection précoce de la maladie d'Alzheimer à partir d'images "FDG-PET scan". [Doctoral Dissertation]. Evry-Val d'Essonne; 2013. Available from: http://www.theses.fr/2013EVRY0005


University of Texas – Austin

8. Bhojanapalli, Venkata Sesha Pavana Srinadh. Large scale matrix factorization with guarantees: sampling and bi-linearity.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 Low rank matrix factorization is an important step in many high dimensional machine learning algorithms. Traditional algorithms for factorization do not scale well with the… (more)

Subjects/Keywords: Matrix completion; Non-convex optimization; Low rank approximation; Semi-definite optimization; Tensor factorization; Scalable algorithms

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APA (6th Edition):

Bhojanapalli, V. S. P. S. (2015). Large scale matrix factorization with guarantees: sampling and bi-linearity. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 26, 2019. http://hdl.handle.net/2152/32832.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 26 Apr 2019.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2152/32832.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32832

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Saskatchewan

9. Pourheidari, Vahid 1988-. Cross domain recommender systems using matrix and tensor factorization.

Degree: 2019, University of Saskatchewan

 Today, the amount and importance of available data on the internet are growing exponentially. These digital data has become a primary source of information and… (more)

Subjects/Keywords: recommender system; cross-domain recommendation; coupled matrix factorization; tensor decomposition; collaborative filtering; hybrid recommender system

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APA (6th Edition):

Pourheidari, V. 1. (2019). Cross domain recommender systems using matrix and tensor factorization. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/11904

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pourheidari, Vahid 1988-. “Cross domain recommender systems using matrix and tensor factorization.” 2019. Thesis, University of Saskatchewan. Accessed April 26, 2019. http://hdl.handle.net/10388/11904.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pourheidari, Vahid 1988-. “Cross domain recommender systems using matrix and tensor factorization.” 2019. Web. 26 Apr 2019.

Vancouver:

Pourheidari V1. Cross domain recommender systems using matrix and tensor factorization. [Internet] [Thesis]. University of Saskatchewan; 2019. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10388/11904.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pourheidari V1. Cross domain recommender systems using matrix and tensor factorization. [Thesis]. University of Saskatchewan; 2019. Available from: http://hdl.handle.net/10388/11904

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université de Lorraine

10. Diop, Mamadou. Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model.

Degree: Docteur es, Automatique, Traitement du signal et des images, Génie informatique, 2018, Université de Lorraine

Cette thèse aborde le problème de la décomposition booléenne des tableaux multidimensionnels de données binaires par modèle de mélange post non-linéaire. Dans la première partie,… (more)

Subjects/Keywords: Factorisation de matrices binaires; Factorisation de tenseurs binaires; Produit matriciel booléen; Rang booléen; Binary matrix factorization; Binary tensor factorization; Boolean matrix product; Boolean rank; 621.382 2; 518

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APA (6th Edition):

Diop, M. (2018). Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model. (Doctoral Dissertation). Université de Lorraine. Retrieved from http://www.theses.fr/2018LORR0222

Chicago Manual of Style (16th Edition):

Diop, Mamadou. “Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model.” 2018. Doctoral Dissertation, Université de Lorraine. Accessed April 26, 2019. http://www.theses.fr/2018LORR0222.

MLA Handbook (7th Edition):

Diop, Mamadou. “Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model.” 2018. Web. 26 Apr 2019.

Vancouver:

Diop M. Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model. [Internet] [Doctoral dissertation]. Université de Lorraine; 2018. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2018LORR0222.

Council of Science Editors:

Diop M. Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire : Boolean decomposition of binary multidimensional arrays using a post nonlinear mixture model. [Doctoral Dissertation]. Université de Lorraine; 2018. Available from: http://www.theses.fr/2018LORR0222


EPFL

11. Leonardi, Nora. Dynamic brain networks explored by structure-revealing methods.

Degree: 2014, EPFL

 The human brain is a complex system able to continuously adapt. How and where brain activity is modulated by behavior can be studied with functional… (more)

Subjects/Keywords: brain imaging; fMRI; resting state; functional connectivity; dynamic networks; graph wavelets; matrix factorization; tensor decomposition; sparsity; multiple sclerosis; aging

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APA (6th Edition):

Leonardi, N. (2014). Dynamic brain networks explored by structure-revealing methods. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/199551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Leonardi, Nora. “Dynamic brain networks explored by structure-revealing methods.” 2014. Thesis, EPFL. Accessed April 26, 2019. http://infoscience.epfl.ch/record/199551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Leonardi, Nora. “Dynamic brain networks explored by structure-revealing methods.” 2014. Web. 26 Apr 2019.

Vancouver:

Leonardi N. Dynamic brain networks explored by structure-revealing methods. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Apr 26]. Available from: http://infoscience.epfl.ch/record/199551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Leonardi N. Dynamic brain networks explored by structure-revealing methods. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/199551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. García Durán, Alberto. Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications.

Degree: Docteur es, Technologies de l'Information et des Systèmes, 2016, Compiègne

Internet offre une énorme quantité d’informations à portée de main et dans une telle variété de sujets, que tout le monde est en mesure d’accéder… (more)

Subjects/Keywords: Apprentissage relationnel; Fonctions d'énergie; Relational learning; Tensor factorization; Embedding models; Energy functions; Link prediction; Question generation; Knowledge bases; Deep learning

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APA (6th Edition):

García Durán, A. (2016). Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications. (Doctoral Dissertation). Compiègne. Retrieved from http://www.theses.fr/2016COMP2271

Chicago Manual of Style (16th Edition):

García Durán, Alberto. “Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications.” 2016. Doctoral Dissertation, Compiègne. Accessed April 26, 2019. http://www.theses.fr/2016COMP2271.

MLA Handbook (7th Edition):

García Durán, Alberto. “Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications.” 2016. Web. 26 Apr 2019.

Vancouver:

García Durán A. Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications. [Internet] [Doctoral dissertation]. Compiègne; 2016. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2016COMP2271.

Council of Science Editors:

García Durán A. Learning representations in multi-relational graphs : algorithms and applications : Apprentissage de représentations en données multi-relationnelles : algorithmes et applications. [Doctoral Dissertation]. Compiègne; 2016. Available from: http://www.theses.fr/2016COMP2271


University of Texas – Austin

13. -1260-0121. Computational methods for understanding genetic variations from next generation sequencing data.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 Studies of human genetic variation reveal critical information about genetic and complex diseases such as cancer, diabetes and heart disease, ultimately leading towards improvements in… (more)

Subjects/Keywords: Genetic variation; Next-generation sequencing; Haplotype assembly; Quasispecies reconstruction; Sequential Monte Carlo; Bayesian inference; Tensor factorization

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APA (6th Edition):

-1260-0121. (2018). Computational methods for understanding genetic variations from next generation sequencing data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68413

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-1260-0121. “Computational methods for understanding genetic variations from next generation sequencing data.” 2018. Thesis, University of Texas – Austin. Accessed April 26, 2019. http://hdl.handle.net/2152/68413.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-1260-0121. “Computational methods for understanding genetic variations from next generation sequencing data.” 2018. Web. 26 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1260-0121. Computational methods for understanding genetic variations from next generation sequencing data. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2152/68413.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-1260-0121. Computational methods for understanding genetic variations from next generation sequencing data. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68413

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

14. Stamile, Claudio. Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques.

Degree: Docteur es, Recherche clinique. Innovation technologique. Santé publique, 2017, Lyon; Université catholique de Louvain (1970-....)

 L’imagerie de résonance magnétique de diffusion (dMRI) est une technique très sensible pour la tractographie des fibres de substance blanche et la caractérisation de l’intégrité… (more)

Subjects/Keywords: Analyse longitudinale; Imagerie par tenseur de diffusion; Imagerie par résonance magnétique; Sclérose en plaques; Extraction des faisceaux de SB; Factorisation de matrices non-négatives; Factorisation tensorielle; Longitudinal analysis; Diffusion tensor imaging; Magnetic resonance imaging; Multiple sclerosis; Fiber-bundle clustering; Non-negative matrix factorization; Tensor factorization; 610

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Stamile, C. (2017). Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques. (Doctoral Dissertation). Lyon; Université catholique de Louvain (1970-....). Retrieved from http://www.theses.fr/2017LYSE1147

Chicago Manual of Style (16th Edition):

Stamile, Claudio. “Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques.” 2017. Doctoral Dissertation, Lyon; Université catholique de Louvain (1970-....). Accessed April 26, 2019. http://www.theses.fr/2017LYSE1147.

MLA Handbook (7th Edition):

Stamile, Claudio. “Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques.” 2017. Web. 26 Apr 2019.

Vancouver:

Stamile C. Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques. [Internet] [Doctoral dissertation]. Lyon; Université catholique de Louvain (1970-....); 2017. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2017LYSE1147.

Council of Science Editors:

Stamile C. Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis : Modèles Non Supervisé pour l’Analyse des Fibres de Substance Blanche dans la Sclérose en Plaques. [Doctoral Dissertation]. Lyon; Université catholique de Louvain (1970-....); 2017. Available from: http://www.theses.fr/2017LYSE1147

15. Cordolino Sobral, Andrews. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.

Degree: Docteur es, Informatique et applications, 2017, La Rochelle

Dans ce manuscrit de thèse, nous introduisons les avancées récentes sur la décomposition en matrices (et tenseurs) de rang faible et parcimonieuse ainsi que les… (more)

Subjects/Keywords: Détection d’objets mobiles; Soustraction de fond; ACP robuste; Décomposition en rang faible et parcimonieuse; Moving object detection; Background/foreground separation; Low-rank and sparse representation; Matrix decomposition tensor factorization

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APA (6th Edition):

Cordolino Sobral, A. (2017). Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. (Doctoral Dissertation). La Rochelle. Retrieved from http://www.theses.fr/2017LAROS007

Chicago Manual of Style (16th Edition):

Cordolino Sobral, Andrews. “Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.” 2017. Doctoral Dissertation, La Rochelle. Accessed April 26, 2019. http://www.theses.fr/2017LAROS007.

MLA Handbook (7th Edition):

Cordolino Sobral, Andrews. “Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.” 2017. Web. 26 Apr 2019.

Vancouver:

Cordolino Sobral A. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. [Internet] [Doctoral dissertation]. La Rochelle; 2017. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2017LAROS007.

Council of Science Editors:

Cordolino Sobral A. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. [Doctoral Dissertation]. La Rochelle; 2017. Available from: http://www.theses.fr/2017LAROS007


Indian Institute of Science

16. Pai, Nithish. A GPU Accelerated Tensor Spectral Method for Subspace Clustering.

Degree: 2016, Indian Institute of Science

 In this thesis we consider the problem of clustering the data lying in a union of subspaces using spectral methods. Though the data generated may… (more)

Subjects/Keywords: Subspace Clustering; Tensors Spectral Method; Hypergraphs and Tensors; Uniform Hypergraph Partitioning Algorithm; Tensor Factorization; Spectral Clustering based Algorithms; GPU Accelerated Algorithm; GPU Computing; Computer Science

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APA (6th Edition):

Pai, N. (2016). A GPU Accelerated Tensor Spectral Method for Subspace Clustering. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/handle/2005/2837 ; http://etd.ncsi.iisc.ernet.in/abstracts/3688/G27278-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pai, Nithish. “A GPU Accelerated Tensor Spectral Method for Subspace Clustering.” 2016. Thesis, Indian Institute of Science. Accessed April 26, 2019. http://etd.iisc.ernet.in/handle/2005/2837 ; http://etd.ncsi.iisc.ernet.in/abstracts/3688/G27278-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pai, Nithish. “A GPU Accelerated Tensor Spectral Method for Subspace Clustering.” 2016. Web. 26 Apr 2019.

Vancouver:

Pai N. A GPU Accelerated Tensor Spectral Method for Subspace Clustering. [Internet] [Thesis]. Indian Institute of Science; 2016. [cited 2019 Apr 26]. Available from: http://etd.iisc.ernet.in/handle/2005/2837 ; http://etd.ncsi.iisc.ernet.in/abstracts/3688/G27278-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pai N. A GPU Accelerated Tensor Spectral Method for Subspace Clustering. [Thesis]. Indian Institute of Science; 2016. Available from: http://etd.iisc.ernet.in/handle/2005/2837 ; http://etd.ncsi.iisc.ernet.in/abstracts/3688/G27278-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. Kaya, Oguz. High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs.

Degree: Docteur es, Informatique, 2017, Lyon

 La factorisation des tenseurs est au coeur des méthodes d'analyse des données massives multidimensionnelles dans de nombreux domaines, dont les systèmes de recommandation, les graphes,… (more)

Subjects/Keywords: Décompositions des tenseurs; Algorithmes parallèles; Partitionnement des hypergraphes; Factorisation des matrices; Arbres de dimension; Tensor decompositions; Parallel algorithms; Hypergraph partitioning; Matrix factorization; Dimension trees

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APA (6th Edition):

Kaya, O. (2017). High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2017LYSEN051

Chicago Manual of Style (16th Edition):

Kaya, Oguz. “High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs.” 2017. Doctoral Dissertation, Lyon. Accessed April 26, 2019. http://www.theses.fr/2017LYSEN051.

MLA Handbook (7th Edition):

Kaya, Oguz. “High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs.” 2017. Web. 26 Apr 2019.

Vancouver:

Kaya O. High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs. [Internet] [Doctoral dissertation]. Lyon; 2017. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2017LYSEN051.

Council of Science Editors:

Kaya O. High Performance Parallel Algorithms for Tensor Decompositions : Algorithmes Parallèles pour les Décompositions des Tenseurs. [Doctoral Dissertation]. Lyon; 2017. Available from: http://www.theses.fr/2017LYSEN051


Indian Institute of Science

18. Pai, Nithish. A GPU Accelerated Tensor Spectral Method for Subspace Clustering.

Degree: 2016, Indian Institute of Science

 In this thesis we consider the problem of clustering the data lying in a union of subspaces using spectral methods. Though the data generated may… (more)

Subjects/Keywords: Subspace Clustering; Tensors Spectral Method; Hypergraphs and Tensors; Uniform Hypergraph Partitioning Algorithm; Tensor Factorization; Spectral Clustering based Algorithms; GPU Accelerated Algorithm; GPU Computing; Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pai, N. (2016). A GPU Accelerated Tensor Spectral Method for Subspace Clustering. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/2837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pai, Nithish. “A GPU Accelerated Tensor Spectral Method for Subspace Clustering.” 2016. Thesis, Indian Institute of Science. Accessed April 26, 2019. http://hdl.handle.net/2005/2837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pai, Nithish. “A GPU Accelerated Tensor Spectral Method for Subspace Clustering.” 2016. Web. 26 Apr 2019.

Vancouver:

Pai N. A GPU Accelerated Tensor Spectral Method for Subspace Clustering. [Internet] [Thesis]. Indian Institute of Science; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2005/2837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pai N. A GPU Accelerated Tensor Spectral Method for Subspace Clustering. [Thesis]. Indian Institute of Science; 2016. Available from: http://hdl.handle.net/2005/2837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

19. Ge, Rong. Provable Algorithms for Machine Learning Problems .

Degree: PhD, 2013, Princeton University

 Modern machine learning algorithms can extract useful information from text, images and videos. All these applications involve solving NP-hard problems in average case using heuristics.… (more)

Subjects/Keywords: machine learning; matrix factorization; provable algorithms; tensor decomposition; topic models

Tensor Decomposition for General Matrix Factorization 180 7 Orthogonal Tensor Decomposition… …entire deep network. 1.3 Tensor Decomposition for General Matrix Factorization In the final… …General Matrix Factorization problem to the Orthogonal Tensor Decomposition problem. In fact… …Factorization . . . . . . . . . . . . . . . . 38 2.4.1 The Gadget… …Efficient Factorization under Separability . . . . . . . . . . . . . . . . . 50 2.5.1 Adding… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Ge, R. (2013). Provable Algorithms for Machine Learning Problems . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp019k41zd62n

Chicago Manual of Style (16th Edition):

Ge, Rong. “Provable Algorithms for Machine Learning Problems .” 2013. Doctoral Dissertation, Princeton University. Accessed April 26, 2019. http://arks.princeton.edu/ark:/88435/dsp019k41zd62n.

MLA Handbook (7th Edition):

Ge, Rong. “Provable Algorithms for Machine Learning Problems .” 2013. Web. 26 Apr 2019.

Vancouver:

Ge R. Provable Algorithms for Machine Learning Problems . [Internet] [Doctoral dissertation]. Princeton University; 2013. [cited 2019 Apr 26]. Available from: http://arks.princeton.edu/ark:/88435/dsp019k41zd62n.

Council of Science Editors:

Ge R. Provable Algorithms for Machine Learning Problems . [Doctoral Dissertation]. Princeton University; 2013. Available from: http://arks.princeton.edu/ark:/88435/dsp019k41zd62n

20. Trouillon, Théo. Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs.

Degree: Docteur es, Mathématiques et Informatique, 2017, Grenoble Alpes

 L'explosion de données relationnelles largement disponiblessous la forme de graphes de connaissances a permisle développement de multiples applications, dont les agents personnels automatiques,les systèmes de… (more)

Subjects/Keywords: Apprentissage statistique; Factorisation de tenseur; Données multi-Relationnelles; Embeddings; Knowledge graph; Link prediction; Machine learning; Tensor factorization; Multi-Relational data; Embeddings; Graphe de connaissances; Prédiction de liens; 004

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APA (6th Edition):

Trouillon, T. (2017). Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2017GREAM048

Chicago Manual of Style (16th Edition):

Trouillon, Théo. “Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs.” 2017. Doctoral Dissertation, Grenoble Alpes. Accessed April 26, 2019. http://www.theses.fr/2017GREAM048.

MLA Handbook (7th Edition):

Trouillon, Théo. “Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs.” 2017. Web. 26 Apr 2019.

Vancouver:

Trouillon T. Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2017. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2017GREAM048.

Council of Science Editors:

Trouillon T. Modèles d'embeddings à valeurs complexes pour les graphes de connaissances : Complex-Valued Embedding Models for Knowledge Graphs. [Doctoral Dissertation]. Grenoble Alpes; 2017. Available from: http://www.theses.fr/2017GREAM048

21. Traa, Johannes. Phase difference and tensor factorization models for audio source separation.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 Audio source separation is a well-known problem in the speech community. Many methods have been proposed to isolate speech signals from a multichannel mixture. In… (more)

Subjects/Keywords: Nonnegative matrix factorization; Nonnegative tensor factorization; Interchannel phase differences; Audio Source Separation

…methods further and incorporate them into several matrix and tensor factorization algorithms… …clearly many matrix and tensor factorization approaches to audio source separation. In this… …will discuss the details of these spatial filters in Chapter 4 in the context of tensor… …separation results corresponding to Figures 3.5-3.8. 28 CHAPTER 4 MATRIX AND TENSOR… …FACTORIZATION MODELS All of the previous methods focused on modeling the IPD features exclusively. In… 

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APA (6th Edition):

Traa, J. (2016). Phase difference and tensor factorization models for audio source separation. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95277

Chicago Manual of Style (16th Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 26, 2019. http://hdl.handle.net/2142/95277.

MLA Handbook (7th Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Web. 26 Apr 2019.

Vancouver:

Traa J. Phase difference and tensor factorization models for audio source separation. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2142/95277.

Council of Science Editors:

Traa J. Phase difference and tensor factorization models for audio source separation. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95277

22. Xu, Yangyang. Block Coordinate Descent for Regularized Multi-convex Optimization.

Degree: MA, Engineering, 2013, Rice University

 This thesis considers regularized block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. I… (more)

Subjects/Keywords: Block multi-convex; Block coordinate descent; Kurdyka-Lojasiewicz inequality; Nonnegative matrix and tensor factorization; Matrix completion; Tensor completion; Optimization

…1.2.4 Nonnegative tensor factorization Nonnegative tensor factorization (NTF) is a… …efficiency on nonnegative tensor factorization and completion. 1.5 Contributions Motivated by… …x29; nonnegative matrix/tensor factorization and (ii) nonnegative matrix/tensor… …Algorithm 1 is applied to both the nonnegative matrix/tensor factorization problem and the… …objective over all variables can be very expensive such as the tensor decomposition [44]… 

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APA (6th Edition):

Xu, Y. (2013). Block Coordinate Descent for Regularized Multi-convex Optimization. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/72066

Chicago Manual of Style (16th Edition):

Xu, Yangyang. “Block Coordinate Descent for Regularized Multi-convex Optimization.” 2013. Masters Thesis, Rice University. Accessed April 26, 2019. http://hdl.handle.net/1911/72066.

MLA Handbook (7th Edition):

Xu, Yangyang. “Block Coordinate Descent for Regularized Multi-convex Optimization.” 2013. Web. 26 Apr 2019.

Vancouver:

Xu Y. Block Coordinate Descent for Regularized Multi-convex Optimization. [Internet] [Masters thesis]. Rice University; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1911/72066.

Council of Science Editors:

Xu Y. Block Coordinate Descent for Regularized Multi-convex Optimization. [Masters Thesis]. Rice University; 2013. Available from: http://hdl.handle.net/1911/72066

23. Shao, Weixiang. Unsupervised Learning from Multi-view Data.

Degree: 2016, University of Illinois – Chicago

 With the advance of technology, data are often with multiple modalities or coming from multiple sources. Such data are called multi-view data. Usually, multiple views… (more)

Subjects/Keywords: Unsupervised learning; Multi-view data; Clustering; Incomplete multi-view data; Feature Selection; Tensor; Nonnegative matrix factorization

…Clustering on Multiple Incomplete Views via Tensor Factorization With advances in data collection… …tensor factorization process with the sparsity constraint and use it to iteratively push the… …32 Different views for grouping the users in the recommendation systems. 36 The tensor… …collectively complete the kernel matrices. (1; 2). Second, we propose to use tensor for… …integrating multiple views and use a weight tensor to minimize the influence of the incompleteness… 

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APA (6th Edition):

Shao, W. (2016). Unsupervised Learning from Multi-view Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shao, Weixiang. “Unsupervised Learning from Multi-view Data.” 2016. Thesis, University of Illinois – Chicago. Accessed April 26, 2019. http://hdl.handle.net/10027/21188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shao, Weixiang. “Unsupervised Learning from Multi-view Data.” 2016. Web. 26 Apr 2019.

Vancouver:

Shao W. Unsupervised Learning from Multi-view Data. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10027/21188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shao W. Unsupervised Learning from Multi-view Data. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/21188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Bhattacharya, Anirban. Bayesian Semi-parametric Factor Models .

Degree: 2012, Duke University

  Identifying a lower-dimensional latent space for representation of high-dimensional observations is of significant importance in numerous biomedical and machine learning applications. In many such… (more)

Subjects/Keywords: Statistics; Bayesian; Contingency table; Convergence rate; Factor model; High-dimensional; Tensor factorization

…129 ix . . . . . . . . . . . . . . . . . . . . 125 6 Non-negative tensor factorizations… …133 6.2 Tensor decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 134… …6.3 Non-negative tensor decomposition . . . . . . . . . . . . . . . . . . . 135 7 Current… …intimate connections to sparse probabilistic tensor decompositions. 10 1.2 Outline In Chapter… …high-dimensional unordered categorical data modeling to non-negative tensor factorizations… 

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APA (6th Edition):

Bhattacharya, A. (2012). Bayesian Semi-parametric Factor Models . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/5606

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bhattacharya, Anirban. “Bayesian Semi-parametric Factor Models .” 2012. Thesis, Duke University. Accessed April 26, 2019. http://hdl.handle.net/10161/5606.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bhattacharya, Anirban. “Bayesian Semi-parametric Factor Models .” 2012. Web. 26 Apr 2019.

Vancouver:

Bhattacharya A. Bayesian Semi-parametric Factor Models . [Internet] [Thesis]. Duke University; 2012. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10161/5606.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhattacharya A. Bayesian Semi-parametric Factor Models . [Thesis]. Duke University; 2012. Available from: http://hdl.handle.net/10161/5606

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.