Simultaneous Representation Learning and Clustering
for Incomplete Multi-view Data
Abstract
Incomplete multi-view clustering has attracted various attentions from diverse fields. Most existing
methods factorize data to learn a unified representation linearly. Their performance may degrade when
the relations between the unified representation and
data of different views are nonlinear. Moreover,
they need post-processing on the unified representations to extract the clustering indicators, which
separates the consensus learning and subsequent
clustering. To address these issues, in this paper,
we propose a Simultaneous Representation Learning and Clustering (SRLC) method. Concretely,
SRLC constructs similarity matrices to measure the
relations between pair of instances, and learns lowdimensional representations of present instances on
each view and a common probability label matrix
simultaneously. Thus, the nonlinear information
can be reflected by these representations and the
clustering results can obtained from label matrix directly. An efficient iterative algorithm with guaranteed convergence is presented for optimization. Experiments on several datasets demonstrate the advantages of the proposed approach