Spectral clustering with brainstorming process for multi-view data

Abstract

Clustering tasks often requires multiple views rather than a single view to correctly reflect diverse characteristics of the cluster boundaries. The cluster boundaries estimated using a single view are incorrect in general, and those incorrect estimation should be compensated by helps of other views. If each view is independent to other views, incorrect estimations will be mostly revised as the number of views grow. However, on the contrary, as the number of views grow it is almost impossible to avoid dependencies among views, and such dependencies often delude correct estimations. Thus, dependencies among views should be carefully considered in multi-view clustering. This paper proposes a new spectral clustering method to deal with multi-view data and dependencies among views. The proposed method is motivated by the brainstorming process. In the brainstorming process, an instance is regarded as an agenda to be discussed, while each view is considered as a brainstormer. Through the discussion step in the brainstorming process, a brainstormer iteratively suggests their opinions and accepts others' different opinions. To compensate the biases caused by information sharing between brainstormers with dependent opinions, those having independent opinions are more encouraged to discuss together than those with dependent opinions. The conclusion step makes a compromise by merging or concatenating all opinions. The clustering is finally done after the conclusion. Experimental results in three tasks show the effectiveness of the proposed method comparing with ordinary single and multi-view spectral clusterings.