Unsupervised Discriminant Analysis Based on the Local and Non-local Mean
Abstract
Considering the performance of unsupervised discriminant projections (UDP) is gravely influenced by outliers, especially in small training sample size situation, a novel method called unsupervised discriminant analysis (UDA) based on the local and non-local mean for feature extraction is proposed in this paper, which is robust to outliers. It utilizes the local and non-local mean to construct the local and non-local scatter, to some extent, overcomes the discriminant difficulty caused by outliers. Besides, LUDA is computationally more efficient than UDP. Experimental results on ORL, YALE and AR face image databases show that the proposed UDA is more efficient and effective than UDP.
- Publication:
-
Physics Procedia
- Pub Date:
- 2012
- DOI:
- 10.1016/j.phpro.2012.02.289
- Bibcode:
- 2012PhPro..24.1967C
- Keywords:
-
- unsupervised discriminant projections;
- local mean;
- feature extraction