Information-theoretic criteria for the design of compressive subspace classifiers

Journal Article

Using Shannon theory, we derive fundamental, asymptotic limits on the classification of low-dimensional subspaces from compressive measurements. We identify a syntactic equivalence between the classification of subspaces and the communication of codewords over non-coherent, multiple-antenna channels, from which we derive sharp bounds on the number of classes that can be discriminated with low misclassification probability as a function of the signal dimensionality and the signal-to-noise ratio. While the bounds are asymptotic in the limit of high dimension, they provide intuition for classifier design at finite dimension. We validate this intuition via an application to face recognition. © 2014 IEEE.

Full Text

Duke Authors

Cited Authors

  • Nokleby, M; Rodrigues, M; Calderbank, R

Published Date

  • January 1, 2014

Published In

Start / End Page

  • 3067 - 3071

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2014.6854164

Citation Source

  • Scopus