Multiple Graph Learning for Scalable Multi-view Clustering

Abstract

Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

“…For local structure preserving, we summarize the paradigm of the traditional AMVGC with local structure and introduce the terms tr(A (𝑝 ) diag(Z1)A (𝑝 )⊤ ), which can be mathematically derived from numerous methods, including BIMVC [20], MVASM [8], and MGLSMC [13]. With the local term, our objective equation becomes:…”