Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
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The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found to provide improved clustering performance over the K-means. Solving the ONMF model is a challenging optimization problem due to the existence of both orthogonality and nonnegativity constraints, and most of the existing methods directly deal with the orthogonality constraint in its original form via various optimization techniques. In this paper, we propose a new ONMF based clustering formulation that equivalently transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a non-convex penalty (NCP) approach to add the non-convex equality constraints to the objective as penalty terms, leaving simple non-negativity constraints only in the penalized problem. One smooth penalty formulation and one non-smooth penalty formulation are respectively studied, and theoretical conditions for the penalized problems to provide feasible stationary solutions to the ONMF based clustering problem are presented. Experimental results based on both synthetic and real datasets are presented to show that the proposed NCP methods are computationally time efficient, and either match or outperform the existing K-means and ONMF based methods in terms of the clustering performance.
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