Feature Selection with Kernel Class Separability

Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a hi...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 30; H. 9; S. 1534 - 1546
1. Verfasser:	Wang, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.09.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Application software Applied sciences Artificial Intelligence Classification Computer science; control theory; systems Criteria Data processing. List processing. Character string processing Exact sciences and technology Feature evaluation and selection Feature extraction Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Kernel Kernels Maximization Memory organisation. Data processing Numerical stability Optimization Parameter estimation Pattern analysis Pattern classification Pattern Recognition, Automated - methods Robustness Software Strategy Support vector machine classification Support vector machines Tuning Separability Discriminant analysis Search strategy Statistical analysis Kernel Fisher Discriminant Analysis Support Vector Machines Optimization Fisher information Kernel method Kernel class separability Alignment Multidimensional analysis Model matching Pattern classification Vector support machine Selection criterion Pattern analysis Artificial intelligence Numerical stability feature selection
ISSN:	0162-8828, 1939-3539
Online-Zugang:	Volltext
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Zusammenfassung:	Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion. To make this feature selection approach work, the issues of automatic kernel parameter tuning, the numerical stability, and the regularization for multi-parameter optimization are addressed. Theoretical analysis uncovers the relationship of this criterion to the radius-margin bound of the SVMs, the KFDA, and the kernel alignment criterion, providing more insight on using this criterion for feature selection. This criterion is applied to a variety of selection modes with different search strategies. Extensive experimental study demonstrates its efficiency in delivering fast and robust feature selection.
Bibliographie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2007.70799