Deep Sparse Subspace Clustering
This addresses subspace clustering for real-world data with nonlinear structures, representing an incremental deep learning extension of sparse subspace clustering.
The paper tackles the problem of subspace clustering when data do not meet linear subspace assumptions by introducing Deep Sparse Subspace Clustering (DSSC), which uses neural networks to enable nonlinear transformations, resulting in superior performance over 12 existing methods on four real-world datasets.
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC). Regularized by the unit sphere distribution assumption for the learned deep features, DSSC can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by neural networks. One of the appealing advantages brought by DSSC is: when original real-world data do not meet the class-specific linear subspace distribution assumption, DSSC can employ neural networks to make the assumption valid with its hierarchical nonlinear transformations. To the best of our knowledge, this is among the first deep learning based subspace clustering methods. Extensive experiments are conducted on four real-world datasets to show the proposed DSSC is significantly superior to 12 existing methods for subspace clustering.