Deep Subspace Clustering Networks

Code

• github.com/panji1990/Deep-subspace-clustering-networks
  OfficialIn papertf★ 0
• github.com/xifengguo/dsc-net
  pytorch★ 0
• github.com/adidenkov/Deep-Subspace-Clustering
  tf★ 0

Abstract

We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Tasks

Benchmark Results

Reproductions