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Feature Selection with Kernel Class Separability

Wang, Lei


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,...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI: 10.1109/TPAMI.2007.70799


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