Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407382 | Neurocomputing | 2016 | 6 Pages |
Abstract
Diagonal principal component analysis (DiaPCA) is an important method for dimensionality reduction and feature extraction. It usually makes use of the ℓ2-norm criterion for optimization, and is thus sensitive to outliers. In this paper, we present a DiaPCA with non-greedy ℓ1-norm maximization (DiaPCA-L1 non-greedy), which is more robust to outliers. Experimental results on two benchmark datasets show the effectiveness and advantages of our proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qiang Yu, Rong Wang, Xiaojun Yang, Bing Nan Li, Minli Yao,