Paper Info
Title
Low-Rank Preserving t-Linear Projection for Robust Image Feature Extraction
Abstract
AbstractAs the cornerstone for joint dimension reduction and feature extraction, extensive linear projection algorithms were proposed to fit various requirements. When being applied to image data, however, existing methods suffer from representation deficiency since the multi-way structure of the data is (partially) neglected. To solve this problem, we propose a novel Low-Rank Preserving t-Linear Projection (LRP-tP) model that preserves the intrinsic structure of the image data using t-product-based operations. The proposed model advances in four aspects: 1) LRP-tP learns the t-linear projection directly from the tensorial dataset so as to exploit the correlation among the multi-way data structure simultaneously; 2) to cope with the widely spread data errors, e.g., noise and corruptions, the robustness of LRP-tP is enhanced via self-representation learning; 3) LRP-tP is endowed with good discriminative ability by integrating the empirical classification error into the learning procedure; 4) an adaptive graph considering the similarity and locality of the data is jointly learned to precisely portray the data affinity. We devise an efficient algorithm to solve the proposed LRP-tP model using the alternating direction method of multipliers. Extensive experiments on image feature extraction have demonstrated the superiority of LRP-tP compared to the state-of-the-arts.
Year
DOI
Venue
2021
10.1109/TIP.2020.3031813
Periodicals
Keywords
DocType
Volume
Feature extraction, Tensors, Data models, Correlation, Data structures, Training, Principal component analysis, Adaptive graph, low-rank tensor representation, robust feature extraction, t-linear projection learning, tensor-product (t-product)
Journal
30
Issue
ISSN
Citations 
1
1057-7149
0
PageRank 
References 
Authors
0.34
13
4
Name
Order
Citations
PageRank
Xiaolin Xiao1366.57
Yongyong Chen27412.11
Yue-jiao Gong369141.19
Yicong Zhou41822108.83