期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 30, 期 8, 页码 2430-2446出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2019.2923007
关键词
Dictionaries; Sparse matrices; Machine learning; Data models; Manifolds; Laplace equations; Atomic measurements; Robust flexible discriminative dictionary learning; joint subspace recovery; enhanced locality; classification
资金
- National Natural Science Foundation of China [61672365, 61732008, 61725203, 61622305, 61871444, 61572339]
- High-Level Talent of the Six Talent Peak Project of the Jiangsu Province of China [XYDXX-055]
- Fundamental Research Funds for the Central Universities of China [JZ2019HGPA0102]
We 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.
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