Paper Info
Title
Robust High-Order Manifold Constrained Sparse Principal Component Analysis for Image Representation
Abstract
In order to efficiently utilize the information in the data and eliminate the negative effects of outliers in the principal component analysis (PCA) method, in this paper, we propose a novel robust sparse PCA method based on maximum correntropy criterion (MCC) with high-order manifold constraints called the RHSPCA. Compared with the traditional PCA methods, the proposed RHSPCA has the following benefits: 1) the MCC regression term is more robust to outliers than the MSE-based regression term; 2) thanks to the high-order manifold constraints, the low-dimensional representations can preserve the local relations of the data and greatly improve the clustering and classification performance for image processing tasks; and 3) in order to further counteract the adverse effects of outliers, the MCC-based samples’ mean is proposed to better centralize the data. We also propose a new solver based on the half-quadratic technique and accelerated block coordinate update strategy to solve the RHSPCA model. Extensive experimental results show that the proposed method can outperform the state-of-the-art robust PCA methods on a variety of image processing tasks, including reconstruction, clustering, and classification, on outliers contaminated datasets.
Year
DOI
Venue
2019
10.1109/tcsvt.2018.2856827
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
Field
DocType
Principal component analysis,Robustness,Manifolds,Kernel,Image reconstruction,Task analysis
Kernel (linear algebra),Sparse PCA,Pattern recognition,Computer science,Outlier,Image processing,Robustness (computer science),Artificial intelligence,Solver,Cluster analysis,Principal component analysis
Journal
Volume
Issue
ISSN
29
7
1051-8215
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Nan Zhou1152.16
Hong Cheng270365.27
Jing Qin313214.27
Yuanhua Du4643.49
Badong Chen591965.71