Structured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization

Orthogonal nonnegative matrix factorization plays an important role for data clustering and machine learning. In this paper, we propose a new optimization model for orthogonal nonnegative matrix factorization based on the structural properties of orthogonal nonnegative matrix. The new model can be solved by a novel convex relaxation technique which can be employed quite efficiently. Numerical examples in document clustering, image segmentation and hyperspectral unmixing are used to test the performance of the proposed model. The performance of our method is better than the other testing methods in terms of clustering accuracy.