Alignment with intra-class structure can improve classification

Conference Paper

High dimensional data is modeled using low-rank subspaces, and the probability of misclassification is expressed in terms of the principal angles between subspaces. The form taken by this expression motivates the design of a new feature extraction method that enlarges inter-class separation, while preserving intra-class structure. The method can be tuned to emphasize different features shared by members within the same class. Classification performance is compared to that of state-of-the-art methods on synthetic data and on the real face database. The probability of misclassification is decreased when intra-class structure is taken into account.

Full Text

Duke Authors

Cited Authors

  • Huang, J; Qiu, Q; Calderbank, R; Rodrigues, M; Sapiro, G

Published Date

  • August 4, 2015

Published In

Volume / Issue

  • 2015-August /

Start / End Page

  • 1921 - 1925

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781467369978

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2015.7178305

Citation Source

  • Scopus