Supervised dimensionality reduction via sequential semidefinite programming
Date
2008
Authors
Shen, Chunhua
Li, Hongdong
Brooks, Michael
Journal Title
Journal ISSN
Volume Title
Publisher
Pergamon-Elsevier Ltd
Abstract
Many dimensionality reduction problems end up with a trace quotient formulation. Since it is difficult to directly solve the trace quotient problem, traditionally the trace quotient cost function is replaced by an approximation such that the generalized eigenvalue decomposition can be applied. In contrast, we directly optimize the trace quotient in this work. It is reformulated as a quasi-linear semidefinite optimization problem, which can be solved globally and efficiently using standard off-the-shelf semidefinite programming solvers. Also this optimization strategy allows one to enforce additional constraints (for example, sparseness constraints) on the projection matrix. We apply this optimization framework to a novel dimensionality reduction algorithm. The performance of the proposed algorithm is demonstrated in experiments by several UCI machine learning benchmark examples, USPS handwritten digits as well as ORL and Yale face data.
Description
Keywords
Keywords: Artificial intelligence; Demodulation; Learning algorithms; Learning systems; Nonlinear programming; Standards; Dimensionality reduction; Dimensionality reduction algorithms; Face data; Generalized eigenvalue decomposition; Handwritten digits; Linear disc Dimensionality reduction; Linear discriminant analysis; Semidefinite programming
Citation
Collections
Source
Pattern Recognition
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description