Algorithm

Materials science

Spectral clustering

Theoretical computer science

Data mining

Computer science

Embedding

Matrix (chemical analysis)

Graph

Mathematics

Notation

Artificial intelligence

Composite material

Cluster analysis

Correlation clustering

Arithmetic

IEEE Transactions on Knowledge and Data Engineering

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available at <styled-content style="color:[">r</styled-content> gb]0,0,1 <uri>https://github.com/guanyuezhen/UOMvSC</uri> .

Unified One-Step Multi-View Spectral Clustering