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Efficient and Robust Feature Extraction by Maximum Margin Criterion (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Li, Haifeng Jiang, Tao Zhang, Keshu |
| Description | In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two most popular linear dimen-sionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features and LDA is not stable due to the small sample size problem. In this pa-per, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this lin-ear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient. In Advances In Neural Information Processing Systems 16 |
| File Format | |
| Language | English |
| Publisher | MIT Press |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Discriminatory Information Class Separability Small Sample Size Problem Feature Extraction Technique Lin-ear Feature Extractor Serious Stability Problem Extensive Experiment Discriminant Feature New Linear Feature Extractor Robust Feature Extraction Feature Extractor Pattern Recognition Dimensionality Reduction New Feature Extractor Linear Discriminant Analysis Popular Linear Dimen-sionality Reduction Method Principal Component Analysis Maximum Margin Criterion |
| Content Type | Text |
| Resource Type | Article |
