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Poster
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
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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.

Keywords: Unsupervised Learning · Nonlinear Dimensionality Reduction and Manifold Learning · Deep Autoencoders
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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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