Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
This work addresses clustering tasks in pattern recognition and information retrieval, representing an incremental improvement over existing graph-regularized NMF methods.
The paper tackled the problem of improving graph-regularized nonnegative matrix factorization (GrNMF) for clustering by integrating multiple kernel learning to refine the graph structure, resulting in encouraging performance compared to state-of-the-art methods like NMF and GrNMF.
Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of nonnegative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.