PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generalized Spectral Bounds for Sparse LDA
Baback Moghadam, Yair Weiss and Shai Avidan
In: ICML 2006, Pittsburgh(2006).

Abstract

We present a discrete spectral framework for the sparse or cardinality-constrained solution of a generalized Rayleigh quotient. This NPhard combinatorial optimization problem is central to supervised learning tasks such as sparse LDA, feature selection and relevance ranking for classication. 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 ecient 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2151
Deposited By:Yair Weiss
Deposited On:15 July 2006