Spectral regression: A Unified approach for Sparse Subspace Learning

Deng Cai, Xiaofei He, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, information retrieval, and pattern recognition. Some popular methods include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). However, a disadvantage of all these approaches is that the learned projective functions are linear combinations of all the original features, thus it is often difficult to interpret the results. In this paper, we propose a novel dimensionality reduction framework, called Unified Sparse Subspace Learning (USSL), for learning sparse projections. USSL casts the problem of learning the projective functions into a regression framework, which facilitates the use of different kinds of regularizers. By using a L1-norm regularizer (lasso), the sparse projections can be efficiently computed. Experimental results on real world classification and clustering problems demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-82
Number of pages10
ISBN (Print)0769530184, 9780769530185
DOIs
StatePublished - Jan 1 2007
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
Country/TerritoryUnited States
CityOmaha, NE
Period10/28/0710/31/07

ASJC Scopus subject areas

  • Engineering(all)

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