Generalized spectral bounds for sparse LDA

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 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.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Baback Moghaddam, Yair Weiss, Shai Avidan
• Keywords: Generalized Rayleigh Quotient, ICML 2006, Machine Learning, Sparse Lda Algorithms, Sparse Linear Discriminants