| Generalized Spectral Bounds for Sparse LDA |
| Date: | June 2006 |
| MERL Contact: | Joseph Katz |
| Authors: | Moghaddam, B.; Weiss, Y.; Avidan, S. |
| Where Published: | International Conference on Machine Learning (ICML'06) |
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.
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