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You searched for +publisher:"University of Texas – Austin" +contributor:("Chakrabarti, Deepayan"). Showing records 1 – 2 of 2 total matches.

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University of Texas – Austin

1. -8687-0258. A multi-scale framework for graph based machine learning problems.

Degree: Computer Sciences, 2017, University of Texas – Austin

Graph data have become essential in representing and modeling relationships between entities and complex network structures in various domains such as social networks and recommender systems. As a main contributor of the recent Big Data trend, the massive scale of graphs in modern machine learning problems easily overwhelms existing methods and thus sophisticated scalable algorithms are needed for real-world applications. In this thesis, we develop a novel multi-scale framework based on the divide-and-conquer principle as an effective and scalable approach for machine learning tasks involving large sparse graphs. We first demonstrate how our multi-scale framework can be applied to the problem of computing the spectral decomposition of massive graphs, which is one of the most fundamental low-rank matrix approximations used in numerous machine learning tasks. While popular solvers suffer from slow convergence, especially when the desired rank is large, our method exploits the clustering structure of the graph and achieves superior performance compared to existing algorithms in terms of both accuracy and scalability. While the main goal of the divide-and-conquer approach is to efficiently compute solutions for the original problem, the proposed multi-scale framework further admits an attractive but less obvious feature that machine learning problems can benefit from. Particularly, we consider partial solutions of the subproblems computed in the process as localized models of the entire problem. By doing so, we can combine models at multiple scales from local to global and generate a holistic view of the underlying problem to achieve better performance than a single global view. We adapt such multi-scale view for the problems of link prediction in social networks and collaborative filtering in recommender systems with additional side information to obtain a model that can make accurate and robust predictions in a scalable manner. Advisors/Committee Members: Dhillon, Inderjit S. (advisor), Whinston, Andrew B (committee member), Qiu, Lili (committee member), Chakrabarti, Deepayan (committee member).

Subjects/Keywords: Machine learning; Data mining; Spectral decomposition; Low rank approximation; Link prediction; Social network analysis; Recommender systems; Collaborative filtering

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

APA (6th Edition):

-8687-0258. (2017). A multi-scale framework for graph based machine learning problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47407

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):

-8687-0258. “A multi-scale framework for graph based machine learning problems.” 2017. Thesis, University of Texas – Austin. Accessed March 26, 2019. http://hdl.handle.net/2152/47407.

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):

-8687-0258. “A multi-scale framework for graph based machine learning problems.” 2017. Web. 26 Mar 2019.

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

Vancouver:

-8687-0258. A multi-scale framework for graph based machine learning problems. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 26]. Available from: http://hdl.handle.net/2152/47407.

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:

-8687-0258. A multi-scale framework for graph based machine learning problems. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47407

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

2. -7585-6925. Distributed and dynamic factor modeling of online data.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

The domain of data mining and machine learning has expanded rapidly in recent years to include both large-scale distributed and streaming computation. Although many open-source and cloud-based frameworks are available for these tasks, many of which are used in-production by industry, this is a rapidly-evolving technology landscape, and the gap between the academic role of algorithm development and discovery and code available for use with real-world data has grown. In addition, although there is a rich history of mathematical models for streaming data on continuous vector spaces, there has been significantly less work on streaming discrete spaces. However, much if not most of the data available online is composed of high-dimensional sparse counts, such as text corpora and interaction networks. We attempt to help bridge this gap by extending promising Bayesian Poisson factorization and co-factorization models that can be used, for example, to model not only text corpora but also related user interactions in a social network. We construct a dependent process prior that enables dynamic latent factor modeling in the natural probability space of the factors, rather than in the raw data. These models are then scaled to and implemented for distributed compute systems and streaming data. We develop an adaptive hashing method (AdaHash) for lambda architectures that can use latent factors calculated during periodic batch mode updates as a similarity metric for hierarchical grouping, or for finding similar factors to reconcile parameters in a distributed compute scenario. In addition, we develop a novel Hidden Markov variant using particle filters to update prior factors and probabilistically group with new factors in a dynamic inference model (D-GaPS). We show experimentally that the distributed model converges to similar factors as single-process inference, and the dynamic model yields superior quality topics over batch mode alternatives. Empirical studies are presented on the use of a U.S. Senate voting and bill summary data set that is readily interpretable with regard to latent factors. Advisors/Committee Members: Ghosh, Joydeep (advisor), Khurshid, Sarfraz (committee member), Julien, Christine (committee member), Sanghavi, Sujay (committee member), Chakrabarti, Deepayan (committee member).

Subjects/Keywords: Distributed clustering; Dynamic clustering; Matrix factorization; Co-factorization

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

APA (6th Edition):

-7585-6925. (2017). Distributed and dynamic factor modeling of online data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62065

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):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Thesis, University of Texas – Austin. Accessed March 26, 2019. http://hdl.handle.net/2152/62065.

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):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Web. 26 Mar 2019.

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

Vancouver:

-7585-6925. Distributed and dynamic factor modeling of online data. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 26]. Available from: http://hdl.handle.net/2152/62065.

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:

-7585-6925. Distributed and dynamic factor modeling of online data. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62065

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

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