Constrained Sparse Subspace Clustering with Side-Information
This work addresses subspace clustering with side-information for applications like cancer gene expression analysis, but it is incremental as it builds on prior constrained subspace clustering methods.
The paper tackles the problem of subspace clustering with partial side-information by proposing CSSC+, an enhanced approach that uses side-information in both affinity matrix learning and spectral clustering stages, and validates it on three cancer gene expression datasets.
Subspace clustering refers to the problem of segmenting high dimensional data drawn from a union of subspaces into the respective subspaces. In some applications, partial side-information to indicate "must-link" or "cannot-link" in clustering is available. This leads to the task of subspace clustering with side-information. However, in prior work the supervision value of the side-information for subspace clustering has not been fully exploited. To this end, in this paper, we present an enhanced approach for constrained subspace clustering with side-information, termed Constrained Sparse Subspace Clustering plus (CSSC+), in which the side-information is used not only in the stage of learning an affinity matrix but also in the stage of spectral clustering. Moreover, we propose to estimate clustering accuracy based on the partial side-information and theoretically justify the connection to the ground-truth clustering accuracy in terms of the Rand index. We conduct experiments on three cancer gene expression datasets to validate the effectiveness of our proposals.