Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
Lai Wei Shanshan Song
Multiview subspace clustering (MVSC) has attracted an increasing amount of
attention in recent years. Most existing MVSC methods first collect
complementary information from different views and consequently derive a
consensus reconstruction coefficient matrix to indicate the subspace structure
of a multi-view data set. In this paper, we initially assume the existence of a
consensus reconstruction coefficient matrix and then use it to build a
consensus graph filter. In each view, the filter is employed for smoothing the
data and designing a regularizer for the reconstruction coefficient matrix.
Finally, the obtained reconstruction coefficient matrices from different views
are used to create constraints for the consensus reconstruction coefficient
matrix. Therefore, in the proposed method, the consensus reconstruction
coefficient matrix, the consensus graph filter, and the reconstruction
coefficient matrices from different views are interdependent. We provide an
optimization algorithm to obtain their optimal values. Extensive experiments on
diverse multi-view data sets demonstrate that our approach outperforms some
state-of-the-art methods.