Subspace clustering

To achieve simultaneously feature learning and subspace clustering, we propose an end-to-end trainable framework called the Self-Supervised Convolutional Subspace Clustering Network (S2ConvSCN) that combines a ConvNet module (for feature learning), a self-expression module (for subspace clustering) and a spectral clustering module (for self-supervision) into a joint optimization framework.

In this work, we first consider a general formulation and provide a unified theoretical guarantee of the block diagonal property of the solutions.

We propose the mixture of Gaussian regression (MoG Regression) for subspace clustering. The MoG Regression seeks a valid way to model the unknown noise distribution, which approaches the real one as far as possible.

This paper proposes a Transformation Invariant Subspace Clustering framework by jointly aligning data samples and learning subspace representation.

We propose an image annotation framework which sequentially performs tag completion and refinement

We propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures.

We formulate the tag completion problem in a subspace clustering model which assumes that images are sampled from subspaces, and complete the tags using the state-of-the-art Low Rank Representation (LRR) method.

In this paper, we propose a new subspace clustering method by employing the MoG model to describe the distribution of complex noise.

In this paper, we analyze the grouping effect of representation based methods in depth.

In this paper, we analyze the grouping effect of representation based methods in depth.