Learning without labels and nonnegative tensor factorization

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/33926

Title: Learning without labels and nonnegative tensor factorization
Author: Balasubramanian, Krishnakumar
Abstract: Supervised learning tasks like building a classifier, estimating the error rate of the predictors, are typically performed with labeled data. In most cases, obtaining labeled data is costly as it requires manual labeling. On the other hand, unlabeled data is available in abundance. In this thesis, we discuss methods to perform supervised learning tasks with no labeled data. We prove consistency of the proposed methods and demonstrate its applicability with synthetic and real world experiments. In some cases, small quantities of labeled data maybe easily available and supplemented with large quantities of unlabeled data (semi-supervised learning). We derive the asymptotic efficiency of generative models for semi-supervised learning and quantify the effect of labeled and unlabeled data on the quality of the estimate. Another independent track of the thesis is efficient computational methods for nonnegative tensor factorization (NTF). NTF provides the user with rich modeling capabilities but it comes with an added computational cost. We provide a fast algorithm for performing NTF using a modified active set method called block principle pivoting method and demonstrate its applicability to social network analysis and text mining.
Type: Thesis
URI: http://hdl.handle.net/1853/33926
Date: 2010-04-08
Publisher: Georgia Institute of Technology
Subject: Unsupervised
Supervised
Latent vatiable
Classification
Regression
Tensor
Nonnegative
Block principal pivoting
ANLS
Machine learning
Artificial intelligence
Supervised learning (Machine learning)
Calculus of tensors
Department: Computing
Advisor: Committee Chair: Lebanon, Guy; Committee Co-Chair: Park, Haesun; Committee Member: Gray, Alexander
Degree: M.S.

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