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 discuss the potential connection to the true clustering accuracy. We conduct experiments on three cancer gene expression datasets to validate the effectiveness of our proposals.

本文提出了一种增强的受限高维子空间聚类方法，称为带约束稀疏子空间聚类加（CSSC +），该方法利用部分辅助信息不仅在学习相似度矩阵阶段而且在谱聚类阶段中使用，同时基于部分辅助信息提出了聚类准确性估计的算法，并在三个癌症基因表达数据集上进行了实验证实其有效性。

具有辅助信息的约束稀疏子空间聚类