Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix

Abstract

Multi-view spectral clustering aims to group data into different categories by optimally exploring complementary information from multiple Laplacian matrices. However, existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct an optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. In this paper, we propose a novel optimal neighborhood multi-view spectral clustering (ONMSC) algorithm to address these issues. Specifically, the proposed algorithm generates an optimal Laplacian matrix by searching the neighborhood of both the linear combination of the first-order and high-order base Laplacian matrices simultaneously. This design enhances the representative capacity of the optimal Laplacian and better utilizes the hidden high-order connection information, leading to improved clustering performance. An efficient algorithm with proved convergence is designed to solve the resultant optimization problem. Extensive experimental results on 9 datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which verifies the effectiveness and advantages of the proposed ONMSC.

Deep multi-view learning methods: A review

“…Recently, lots of traditional multi-view spectral clustering approaches also have been applied into many practical fields, such as co-regularized SC (CRSC) [129], co-training SC (CTSC) [16], convex sparse SC [130], multi-view SC co-clustering [131] and [132]. Li et al [133] extend it to multi-view scenario as follows…”

Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

“…However, most anchor point selection applies heuristic sampling strategies, such as k-means or random sampling, which prevents the mutual negotiation between anchor point selection and graph construction to achieve optimal clustering. Besides, existing graph-based methods usually consist of two stages, which construct graphs from raw data first, and perform post-processing to obtain the final result then (Zhou et al 2020b;Gao et al 2020;Liu et al 2019). The final cluster structure is not clearly shown in the graph constructed in the previous stage, and the clustering performance dependents heavily on the constructed graphs (Nie et al 2016b(Nie et al , 2017.…”

Multiple Kernel k-Means With Low-Rank Neighborhood Kernel

“…These solutions fuse information either in different ways or in different stages of the training process. The third category is multi-view graph learning, where data items are represented by vertices and their relationships are represented by edges of the graph [17], [18]. To formalize, the graph matrix is often constructed via similarity matrix which quantifies the affinity among different objects [19].…”