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Title:Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
Author(s):Cai, Deng
Doctoral Committee Chair(s):Han, Jiawei
Doctoral Committee Member(s):Huang, Thomas S.; Zhai, ChengXiang; Chang, Kevin C-C.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Machine Learning
Abstract:Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms only provide the embedding results of training samples. There are many extensions of these approaches which try to solve the out-of-sample extension problem by seeking an embedding function in reproducing kernel Hilbert space. However, a disadvantage of all these approaches is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this thesis, we introduce a novel dimensionality reduction framework, called {\bf Spectral Regression} (SR). SR casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression as a building block, different kinds of regularizers can be naturally incorporated into our framework which makes it more flexible. SR can be performed in supervised, unsupervised and semi-supervised situation. It can make efficient use of both labeled and unlabeled points to discover the intrinsic discriminant structure in the data. We have applied our algorithms to several real world applications, e.g. face analysis, document representation and content-based image retrieval.
Issue Date:2009
Citation Info:Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2009
Genre:Dissertation / Thesis
Type:Text
Language:English
URI:http://hdl.handle.net/2142/11702
Publication Status:published or submitted for publication
Peer Reviewed:not peer reviewed
Rights Information:Copyright 2009 Deng Cai
Date Available in IDEALS:2009-05-05


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