Title
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
Abstract
AbstractWe propose a joint subspace recovery and enhanced locality-based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL). The RFDDL mainly improves the data representation and classification abilities by enhancing the robust property to sparse errors and encoding the locality, reconstruction error, and label consistency more accurately. First, for the robustness to noise and sparse errors in data and atoms, the RFDDL aims at recovering the underlying clean data and clean atom subspaces jointly, and then performs DL and encodes the locality in the recovered subspaces. Second, to enable the data sampled from a nonlinear manifold to be handled potentially and obtain the accurate reconstruction by avoiding the overfitting, the RFDDL minimizes the reconstruction error in a flexible manner. Third, to encode the label consistency accurately, the RFDDL involves a discriminative flexible sparse code error to encourage the coefficients to be soft. Fourth, to encode the locality well, the RFDDL defines the Laplacian matrix over recovered atoms, includes label information of atoms in terms of intra-class compactness and inter-class separation, and associates with group sparse codes and classifier to obtain the accurate discriminative locality-constrained coefficients and classifier. The extensive results on public databases show the effectiveness of our RFDDL.
Year
DOI
Venue
2020
10.1109/TCSVT.2019.2923007
Periodicals
Keywords
DocType
Volume
Dictionaries, Sparse matrices, Machine learning, Data models, Manifolds, Laplace equations, Atomic measurements, Robust flexible discriminative dictionary learning, joint subspace recovery, enhanced locality, classification
Journal
30
Issue
ISSN
Citations 
8
1051-8215
5
PageRank 
References 
Authors
0.40
25
7
Name
Order
Citations
PageRank
Zhao Zhang193865.99
Jiahuan Ren2192.22
Weiming Jiang31008.50
Zheng Zhang454940.45
Richang Hong54791176.47
Shuicheng Yan616210.26
Meng Wang73094167.38