Kernel Methods for Nonlinear Discriminative Data Analysis
Identifieur interne : 001321 ( Main/Merge ); précédent : 001320; suivant : 001322Kernel Methods for Nonlinear Discriminative Data Analysis
Auteurs : Xiuwen Liu [États-Unis] ; Washington Mio [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.
Url:
DOI: 10.1007/11585978_38
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