Improving support vector data description using local density degree

KiYoung Lee

doi:10.1016/j.patcog.2005.03.020

2005-10-01

PRE

DOI：10.1016/j.patcog.2005.03.020

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摘要

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We propose a new support vector data description (SVDD) incorporating the local density of a training data set by introducing a local density degree for each data point. By using a density-induced distance measure based on the degree, we reformulate a conventional SVDD. Experiments with various real data sets show that the proposed method more accurately describes training data sets than the conventional SVDD in all tested cases. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keyword:

D-SVDD

support vector data description

one-class classification

data domain description

outlier detection