Abstract
Feature selection technology can help to reduce feature redundancy and improve classification performance. Most general feature selection methods do not perform well on high-dimension large-scale data sets of multimedia applications. In this paper we propose a novel feature selection method named Local Separability Assessment. We try to measure the separation level of samples in subregions of feature space, and integrate them for evaluating the separability of features. Our method has favorable performance on large-scale continuous data sets, and requires no priori hypothesis on data distribution. The experiments on various applications have proved its excellence.
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© 2008 Springer-Verlag Berlin Heidelberg
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Tao, K., Lin, SX., Zhang, YD., Tang, S. (2008). Local Separability Assessment: A Novel Feature Selection Method for Multimedia Applications. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_102
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DOI: https://doi.org/10.1007/978-3-540-89796-5_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89795-8
Online ISBN: 978-3-540-89796-5
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