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Deep subspace clustering networks

Ji, Pan; Zhang, Tong; Li, Hongdong; Salzmann, Mathieu; Reid, Ian

Description

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...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
URI: http://hdl.handle.net/1885/205448
Source: Proceedings of the 31st Annual Conference on Neural Information Processing Systems, NIPS 2017
DOI: 978-1-5108-6096-4
Access Rights: Open Access

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