Multi-View Data Fusion Oriented Clustering via Nuclear Norm Minimization

Image clustering remains challenging when handling image data from heterogeneous sources. Fusing the independent and complementary information existing in heterogeneous sources together facilitates to improve the image clustering performance. To this end, we propose a joint learning framework of multi-view image data fusion and clustering based on nuclear norm minimization. Specifically, we first formulate the problem as matrix factorization to a shared clustering indicator matrix and a representative coefficient matrix. The former is constrained with orthogonality and nonnegativity, which ensures the validation of clustering assignments. The latter is imposed with nuclear norm minimization to achieve compression of principal components for performance improvement. Then, an alternating minimization strategy is employed to efficiently decompose the multi-variable optimization problem into several small solvable sub-problems with closed-form solutions. Extensive experimental results on real-world image and video datasets demonstrate the superiority of proposed method over other state-of-the-art methods.