A union of incoherent spaces model for classification

We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low misclassification, the subspaces should be incoherent so that features of a given class cannot represent efficiently signals from another. We propose a simple iterative strategy to learn dictionaries which are are the same time good for approximating within a class and also discriminant. Preliminary tests on a standard face images database show competitive results.


Published in:
Proc. of the 35th IEEE International Conference on Acoustics, Speech and Signal Processing
Presented at:
35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, March 14-19, 2010
Year:
2010
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
Keywords:
Laboratories:




 Record created 2010-01-25, last modified 2018-11-14

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)