Learning Transformations

Published

Conference Paper

© 2014 IEEE. A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature, partitioning such high-dimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often violated for real-world observations, as they can be corrupted by errors or deviate from ideal models. We propose to address this by learning a linear transformation on subspaces using matrix rank, via its convex surrogate nuclear norm, as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same subspace, and, at the same time, forces a high-rank structure for data from different subspaces. In this way, we reduce variations within the subspaces, and increase separation between the subspaces for improved subspace clustering and 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

  • 4008 - 4012

International Standard Book Number 13 (ISBN-13)

  • 9781479957514

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2014.7025814

Citation Source

  • Scopus