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We propose a joint subspace recovery and enhanced locality based robustflexible label consistent dictionary learning method called Robust FlexibleDiscriminative Dictionary Learning (RFDDL). RFDDL mainly improves the datarepresentation and classification abilities by enhancing the robust property tosparse errors and encoding the locality, reconstruction error and labelconsistency more accurately. First, for the robustness to noise and sparseerrors in data and atoms, RFDDL aims at recovering the underlying clean dataand clean atom subspaces jointly, and then performs DL and encodes the localityin the recovered subspaces. Second, to enable the data sampled from a nonlinearmanifold to be handled potentially and obtain the accurate reconstruction byavoiding the overfitting, RFDDL minimizes the reconstruction error in aflexible manner. Third, to encode the label consistency accurately, RFDDLinvolves a discriminative flexible sparse code error to encourage thecoefficients to be soft. Fourth, to encode the locality well, RFDDL defines theLaplacian matrix over recovered atoms, includes label information of atoms interms of intra-class compactness and inter-class separation, and associateswith group sparse codes and classifier to obtain the accurate discriminativelocality-constrained coefficients and classifier. Extensive results on publicdatabases show the effectiveness of our RFDDL.

Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning