Sparse dual regularized concept factorization for image representation

Low-rank matrix factorization is one of the most useful tools in image representation and computer vision. Among of its techniques, Concept Factorization (CF) is a new matrix decomposition technique for data representation. A modified CF algorithm called Sparse Dual Regularized Concept Factorization (SDRCF) is proposed for addressing the limitations of CF and Local Consistent Concept Factorization (LCCF), which did not consider the geometric structure or the label information of the data. SDRCF simultaneously preserves the intrinsic geometry of the data and the feature as regularized term, and preserve the sparse reconstructive relationship of the data. We also present SDRCF as an extension of CF and LCCF. Compared with Non-Negative Matrix Factorization (NMF), Graph NMF (GNMF), CF and LCCF, experiment results on ORL face database and Coil20 image database have shown that the proposed method achieves better clustering results.