Adaptive KNN and graph-based auto-weighted multi-view consensus spectral learning

Abstract

The multi-view learning is a fundamental problem in the multimedia analysis. However, most existing multi-view learning methods need to calculate a similarity matrix for each view. This operation has two drawbacks. Firstly, the construction of the similarity matrix needs a fixed function. But, in fact, the internal relationship between data points changes continuously. The fixed graph representation matrix is not conducive to fully consider the heterogeneous information about different views. Secondly, the similarity matrix of each view is used to participate independently in the subsequent calculation, which ignores the correlation and importance of different views. In our paper, an adaptive K Nearest Neighbor (KNN) and graph-based auto-weighted multi-view consensus spectral clustering (KGWMC) is proposed. An adaptive KNN method is used to calculate a similarity matrix for each view, which is updated in the subsequent iteration process. An adaptive joint optimization strategy is used to fuse multiple views for obtaining a consensus graph. In this step, instead of using a fixed function to calculate the weight of each view, we transform the weight calculation into an optimization problem for iteratively updating the weight of each view in a non-parametric form. The main novelty of KGWMC is the learning method of each similarity matrix and consensus graph in a mutual reinforcement manner. When the consensus graph converges, the spectral clustering algorithm is implemented to it for obtaining the final clustering. Finally, we demonstrate the effectiveness of our algorithm by comparing it with nine state-of-the-art algorithms on six real datasets.