Concept factorization with adaptive graph learning on Stiefel manifold

In machine learning and data mining, concept factorization (CF) has achieved great success for its powerful capability in data representation. To learn an adaptive inherent graph structure of data space, and to ease the burden brought by the explicit orthogonality constraint, we propose a concept factorization with adaptive graph learning on the Stiefel manifold (AGCF-SM). The method essentially integrates concept factorization and manifold learning into a unified framework. Therein the adaptive similarity graph is learned by iterative locally linear embedding, which is free from dependence on neighbor sets. An iterative updating algorithm is developed and the convergence and complexity analyses of the algorithm are provided. The numerical experiments on ten benchmark datasets have demonstrated that the proposed algorithm outperforms other state-of-the-art algorithms.