Alignment with intra-class structure can improve classification

Jiaji Huang; Qiang Qiu; A. Robert Calderbank; Miguel R. D. Rodrigues; Guillermo Sapiro

ICASSP 2015accepted0 citations

Alignment with intra-class structure can improve classification

Jiaji Huang, Qiang Qiu, A. Robert Calderbank, Miguel R. D. Rodrigues, Guillermo Sapiro

Abstract

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.

BibTeX

@inproceedings{icassp2015_alignmentwithint,
  title = {Alignment with intra-class structure can improve classification},
  author = {Jiaji Huang and Qiang Qiu and A. Robert Calderbank and Miguel R. D. Rodrigues and Guillermo Sapiro},
  booktitle = {ICASSP 2015},
  year = {2015}
}