Subspace detection in a kernel space: The missing data case

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

3 Scopus citations

Abstract

This paper studies the problem of matched subspace detection in high-dimensional feature space where the signal in the input space is partially observed. We present a test statistic for our detection problem using kernel functions and provide kernel function value estimators with missing data for different kernels. The test statistic can be calculated approximately with estimated kernel function values. We also give theoretical results regarding the kernel function value and test statistic estimation. Numerical experiments involving both Gaussian and polynomial kernels show the efficacy of the proposed kernel function value estimator and resulting subspace detector.

Original languageEnglish (US)
Title of host publication2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
PublisherIEEE Computer Society
Pages93-96
Number of pages4
ISBN (Print)9781479949755
DOIs
StatePublished - 2014
Event2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 - Gold Coast, QLD, Australia
Duration: Jun 29 2014Jul 2 2014

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Country/TerritoryAustralia
CityGold Coast, QLD
Period6/29/147/2/14

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Signal Processing
  • Electrical and Electronic Engineering
  • Computer Science Applications

Keywords

  • Kernel methods
  • missing data
  • subspace detector

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