Generalized spectral bounds for sparse LDA

Baback Moghaddam, Yair Weiss, Shai Avidan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a discrete spectral framework for the sparse or cardinality-constrained solution of a generalized Rayleigh quotient. This NP-hard combinatorial optimization problem is central to supervised learning tasks such as sparse LDA, feature selection and relevance ranking for classification. We derive a new generalized form of the Inclusion Principle for variational eigenvalue bounds, leading to exact and optimal sparse linear discriminants using branch-and-bound search. An efficient greedy (approximate) technique is also presented. The generalization performance of our sparse LDA algorithms is demonstrated with real-world UCI ML benchmarks and compared to a leading SVM-based gene selection algorithm for cancer classification.

Original languageEnglish
Title of host publicationICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Pages641-648
Number of pages8
StatePublished - 2006
Externally publishedYes
EventICML 2006: 23rd International Conference on Machine Learning - Pittsburgh, PA, United States
Duration: 25 Jun 200629 Jun 2006

Publication series

NameICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Volume2006

Conference

ConferenceICML 2006: 23rd International Conference on Machine Learning
Country/TerritoryUnited States
CityPittsburgh, PA
Period25/06/0629/06/06

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