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Deep Subspace Clustering Networks
Pan Ji · Tong Zhang · Hongdong Li · Mathieu Salzmann · Ian Reid

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #17 #None

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.

Author Information

Pan Ji (NEC Labs America)
Tong Zhang (The Australian National University)
Hongdong Li (Australian National University)
Mathieu Salzmann (EPFL)
Ian Reid (University of Adelaide)

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