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University of Colorado

1. Bertrand, Nicholas. Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.

Degree: MS, Applied Mathematics, 2012, University of Colorado

We investigate the problem of finding a parameterization of a smooth, low-dimensional manifold based on noisy observations from a high-dimensional ambient space. The formulation of such parameterizations sees applications in a variety of areas such as data denoising and image segmentation. We introduce algorithms inspired by the existing k-svd algorithm for training dictionaries for sparse data representation, and the local best-fit at algorithm for hybrid linear modeling. The output of our algorithm is an assignment of input data points to locally linear models. To demonstrate the applicability of our algorithm, we discuss experiments performed on synthetic datasets. Advisors/Committee Members: Francois Meyer, James Curry, Shannon Hughes.

Subjects/Keywords: k-svd algorithm; MLBF; Applied Mathematics

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

APA (6th Edition):

Bertrand, N. (2012). Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/55

Chicago Manual of Style (16th Edition):

Bertrand, Nicholas. “Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.” 2012. Masters Thesis, University of Colorado. Accessed March 07, 2021. https://scholar.colorado.edu/appm_gradetds/55.

MLA Handbook (7th Edition):

Bertrand, Nicholas. “Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.” 2012. Web. 07 Mar 2021.

Vancouver:

Bertrand N. Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2021 Mar 07]. Available from: https://scholar.colorado.edu/appm_gradetds/55.

Council of Science Editors:

Bertrand N. Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. [Masters Thesis]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/appm_gradetds/55


KTH

2. Pettersson, Christoffer. Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing.

Degree: Computer Science and Communication (CSC), 2016, KTH

The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. They contain no information regarding prior labeling or categorization which creates a need for an unsupervised learning approach using solely the raw text based content as data. The project investigates state-of-the-art concepts like bag-of-words for calculating term importance and the gap statistic for determining an optimal number of clusters. The data is vectorized using term frequency - inverse document frequency to determine the importance of terms relative to the document and to all documents combined. An inherit problem of this approach is high dimensionality which is reduced using latent semantic analysis in conjunction with singular value decomposition. Once the resulting clusters have been obtained, the most frequently occurring terms for each cluster are analyzed and compared. Due to the absence of initial labeling an alternative approach is required to evaluate the clusters validity. To do this, the receivers of all emails in each cluster who actively opened an email is collected and investigated. Each receiver have different attributes regarding their purpose of using the service and some personal information. Once gathered and analyzed, conclusions could be drawn that it is possible to find distinguishable connections between the resulting email clusters and their receivers but to a limited extent. The receivers from the same cluster did show similar attributes as each other which were distinguishable from the receivers of other clusters. Hence, the resulting email clusters and their receivers are specific enough to distinguish themselves from each other but too general to handle more detailed information. With more data, this could become a useful tool for determining which users of a service should receive a particular email to increase the conversion rate and thereby reach out to more relevant people based on previous trends.

Målet med detta projekt att undersöka eventuella samband mellan marknadsföringsemail och dess mottagare med hjälp av oövervakad maskininlärning på en brgränsad mängd data. Datan består av ca 1200 email meddelanden med 98.000 mottagare. Initialt så gruperas alla meddelanden baserat på innehåll via text klustering. Meddelandena innehåller ingen information angående tidigare gruppering eller kategorisering vilket skapar ett behov för ett oövervakat tillvägagångssätt för inlärning där enbart det råa textbaserade meddelandet används som indata. Projektet undersöker moderna tekniker så som bag-of-words för att avgöra termers relevans och the gap statistic för att finna ett optimalt antal kluster. Datan vektoriseras med hjälp av term frequency - inverse document frequency för att avgöra relevansen av termer relativt dokumentet…

Subjects/Keywords: Machine learning; Unsupervised; Natural language processing; nlp; clustering; centroid based; k-means; text clustering; limited data; email clustering; lsa; svd; tf-idf; dimensionality reduction; the gap statistic; Lloyd's algorithm; vectorization; feature extraction; Computer Sciences; Datavetenskap (datalogi)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Pettersson, C. (2016). Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147

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):

Pettersson, Christoffer. “Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing.” 2016. Thesis, KTH. Accessed March 07, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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

MLA Handbook (7th Edition):

Pettersson, Christoffer. “Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing.” 2016. Web. 07 Mar 2021.

Vancouver:

Pettersson C. Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing. [Internet] [Thesis]. KTH; 2016. [cited 2021 Mar 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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

Council of Science Editors:

Pettersson C. Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147

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

.