Self-weighting multi-view spectral clustering based on nuclear norm. (April 2022) - Shi, Shaojun; Nie, Feiping; Wang, Rong; Li, Xuelong
Highlights: In order to implement clustering task, the proposed approach fully utilizes multiple view features to learn a consensus representation. Specifically, it makes the common consensus representation be close to each view initial affinity matrix as much as possible. Moreover, to capture principal components of different views, the nuclear norm is applied to the learned consensus representation. Considering that each view feature is inclined to explore specific properties, therefore, the proposed method assigns different weight for each view feature in the form of exponential flatten. An efficient alternative iteration algorithm is exploited to solve the proposed optimization problem. In addition, extensive experiments are conducted on four multi-view data sets without noises and one multi-view data set with"salt and pepper" noises to demonstrate the superiority of proposed SMSC_NN method. Abstract: Multi-view clustering attracts more and more attention due to the fact that it can utilize the complementary and compatible information from multi-view data sets. In many graph-based multi-view clustering approaches, the graph quality is important since it influences the following clustering performance. Therefore, learning a high quality similarity graph is desired. In this paper, we propose a novel clustering method which is named as Self-weighting Multi-view Spectral Clustering based on Nuclear Norm (SMSC_NN). Specifically, to fully utilize the multiple view features, the common consensus representation is learned. Moreover, to capture the principal components from various view features, the nuclear norm is introduced which can make the view-specific information be well explored. Further, due to the fact that each view feature denotes a sort of specific property, the adaptive weights are assigned instead of equal view weights. In order to verify the effectiveness of the proposed method, four multi-view data sets are used to conduct the clustering experiments. Extensive experimental results demonstrate the superiority of the proposed method comparing with state-of-the-art multi-view clustering approaches. In addition, the proposed approach is experimented on the Cal101-20 data set with "salt and pepper" noises, and experimental results verify that the proposed SMSC_NN method can remain robust to noises.