One-step multi-view spectral clustering based on multi-feature similarity fusion

Multi-view clustering has attracted increasing attention for handling complex data with multiple views or sources. Among them, spectral clustering-based methods become more and more popular due to it can make full use of information from different views. However, most existing multi-view spectral clustering methods typically adopt a two-step scheme, which firstly obtains the spectral embedding matrix through graph fusion or multi-feature fusion, and then uses the k-means algorithm to cluster the spectral embedding matrix to obtain the final clustering result. This two-step scheme inevitably leads to information loss, resulting in a suboptimal solution. Furthermore, the methods of graph fusion and multi-feature fusion have not taken into account the inconsistency of features between different views and the unordered nature of clustering labels, which also decreases the clustering performance. To solve these problems, we propose a novel one-step multi-view spectral clustering based on multi-feature similarity fusion. This model simultaneously conducts graph learning, multi-feature similarity fusion and discretization in a unified framework, which can mutually negotiate and optimize each other to achieve better results. Furthermore, compared to directly fusing affinity matrices or spectral embedding matrixs from different views, we take advantage of the property of the spectral embedding matrix, fuse the similarity of samples in feature space, better handle the differences between different views. Finally, the superiority of our method is verified by the experimental evaluation of several data sets. The demo code of this work is publicly available at https://github.com/kong-de-zheng/MOMSC.