Stochastic subspace search for top-k multi-view clustering

Abstract

Finding multiple clustering solutions has recently gained much attention. Based on the observation that data is often multi-faceted, novel clustering methods have been introduced capable of detecting multiple, diverse clusterings. In this work-in-progress paper, we present a novel stochastic subspace search principle that tackles the requirements of multi-view clustering. The main idea is to consider each subspace as a state in a Markov chain and using Monte Carlo methods to sample the multi-view subspaces. By dynamically adapting the underlying probability density function we realize the generation of alternative clustering views. We present preliminary experimental results of our method and we describe future research directions.

MultiClust 2013: Multiple Clusterings, Multiview Data, and Multisource Knowledgedriven Clustering

“…The contribution by Li et al [9] discusses an approach to multi-view clustering based on a Markov Chain Monte Carlo sampling of relevant subspaces. A subspace is a subset of input features, and is considered to be a state of a Markov chain.…”