Sparse feature extraction for support vector data description applications

Amit Banerjee, Radford Juang, Joshua Broadwater, Philippe Burlina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Support Vector Data Description (SVDD) methods have been successfully applied to hyperspectral anomaly detection. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse features could be identified for these techniques, the resulting data may improve SVDD performance while enjoying the benefits of decreased processing overhead. Although band selection has been investigated in previous efforts, this work builds on recent research that has resulted in the development of a theoretical framework for signal classification with sparse representation using L 1 measures.

Original languageEnglish (US)
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Pages4236-4239
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
Duration: Jul 25 2010Jul 30 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
CountryUnited States
CityHonolulu, HI
Period7/25/107/30/10

Keywords

  • Feature selection
  • Hyperspectral processing
  • Kernel methods

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Banerjee, A., Juang, R., Broadwater, J., & Burlina, P. (2010). Sparse feature extraction for support vector data description applications. In 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 (pp. 4236-4239). [5653539] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2010.5653539