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This paper attempts to represent the mapped data in the radial basis function (RBF) feature space under non-negativity constraints and develops a RBF kernel based non-negative matrix factorization (KNMF-RBF) algorithm. Based on an objective function with Frobenius norm, we obtain the multiplicative update rules of our KNMF-RBF approach using kernel theory and gradient descent method. The proposed...
Based on the kernel method and graph theory, this paper proposes a novel Kernel Non-negative Matrix Factorization with Local and Non-local feature (LN-KNMF) approach for face recognition. We establish the objective function in kernel space which incorporates two scatter quantities, namely local scatter and non-local scatter. They are determined by the local adjacent graph matrix and non-local adjacent...
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