Learning compressed image classification features

Conference Paper

Learning a transformation-based dimension reduction, thereby compressive, technique for classification is here proposed. High-dimensional data often approximately lie in a union of low-dimensional subspaces. We propose to perform dimension reduction by learning a 'fat' linear transformation matrix on subspaces using nuclear norm as the optimization criteria. The learned transformation enables dimension reduction, and, at the same time, restores a low-rank structure for data from the same class and maximizes the separation between different classes, thereby improving classification via learned low-dimensional features. Theoretical and experimental results support the proposed framework, which can be interpreted as learning compressing sensing matrices for classification.

Full Text

Duke Authors

Cited Authors

  • Qiu, Q; Sapiro, G

Published Date

  • January 28, 2014

Published In

  • 2014 Ieee International Conference on Image Processing, Icip 2014

Start / End Page

  • 5761 - 5765

International Standard Book Number 13 (ISBN-13)

  • 9781479957514

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2014.7026165

Citation Source

  • Scopus