Title:
Learning without labels and nonnegative tensor factorization
Learning without labels and nonnegative tensor factorization
Author(s)
Balasubramanian, Krishnakumar
Advisor(s)
Lebanon, Guy
Park, Haesun
Park, Haesun
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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.
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Date Issued
2010-04-08
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Resource Type
Text
Resource Subtype
Thesis