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In many real-world applications, we have access to multiple views of thedata, each of which characterizes the data from a distinct aspect. Severalprevious algorithms have demonstrated that one can achieve better clusteringaccuracy by integrating information from all views appropriately than usingonly an individual view. Owing to the effectiveness of spectral clustering,many multi-view clustering methods are based on it. Unfortunately, they havelimited applicability to large-scale data due to the high computationalcomplexity of spectral clustering. In this work, we propose a novel multi-viewspectral clustering method for large-scale data. Our approach is structuredunder the guided co-training scheme to fuse distinct views, and uses thesampling technique to accelerate spectral clustering. More specifically, wefirst select $p$ ($\ll n$) landmark points and then approximate theeigen-decomposition accordingly. The augmented view, which is essential toguided co-training process, can then be quickly determined by our method. Theproposed algorithm scales linearly with the number of given data. Extensiveexperiments have been performed and the results support the advantage of ourmethod for handling the large-scale multi-view situation.

Guided Co-training for Large-Scale Multi-View Spectral Clustering