Show simple item record

dc.contributor.authorDavenport, Mark A.
Baraniuk, Richard G.
Scott, Clayton D.
dc.creatorBaraniuk, Richard G.
Scott, Clayton D.
Davenport, Mark A. 2007-10-31T00:41:41Z 2007-10-31T00:41:41Z 2006-09-01 2006-09-01
dc.identifier.citation M. A. Davenport, R. G. Baraniuk and C. D. Scott, "Learning minimum volume sets with support vector machines," 2006.
dc.description Conference Paper
dc.description.abstract Given a probability law P on d-dimensional Euclidean space, the minimum volume set (MV-set) with mass beta , 0 < beta < 1, is the set with smallest volume enclosing a probability mass of at least beta. We examine the use of support vector machines (SVMs) for estimating an MV-set from a collection of data points drawn from P, a problem with applications in clustering and anomaly detection. We investigate both one-class and two-class methods. The two-class approach reduces the problem to Neyman-Pearson (NP) classification, where we artificially generate a second class of data points according to a uniform distribution. The simple approach to generating the uniform data suffers from the curse of dimensionality. In this paper we (1) describe the reduction of MV-set estimation to NP classification, (2) devise improved methods for generating artificial uniform data for the two-class approach, (3) advocate a new performance measure for systematic comparison of MV-set algorithms, and (4) establish a set of benchmark experiments to serve as a point of reference for future MV-set algorithms. We find that, in general, the two-class method performs more reliably.
dc.language.iso eng
dc.title Learning minimum volume sets with support vector machines
dc.type Conference paper 2006-09-13
dc.citation.bibtexName inproceedings 2006-09-13
dc.citation.location Maynooth, Ireland
dc.citation.conferenceName IEEE Workshop on Machine Learning for Signal Processing (MLSP)
dc.type.dcmi Text
dc.type.dcmi Text
dc.citation.firstpage 301
dc.citation.lastpage 306

Files in this item


This item appears in the following Collection(s)

  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1401]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

Show simple item record