Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117917
Type: | Conference paper |
Title: | Deep subspace clustering networks |
Author: | Ji, P. T. Zhang, H. Li, M. Salzmann, Reid, I. |
Citation: | Advances in neural information processing systems, 2017 / Guyon, I., Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (ed./s), vol.2017-December, pp.1-10 |
Publisher: | Neural Information Processing Systems Foundation |
Issue Date: | 2017 |
Series/Report no.: | NIPS Proceedings |
ISSN: | 1049-5258 |
Conference Name: | Conference on Neural Information Processing Systems (NIPS) (4 Dec 2017 - 9 Dec 2017 : Long Beach, CA) |
Editor: | Guyon, I. Luxburg, U. Bengio, S. Wallach, H. Fergus, R. Vishwanathan, S. Garnett, R. |
Statement of Responsibility: | Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid |
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. |
Rights: | Copyright status unknown |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/FL130100102 http://purl.org/au-research/grants/arc/DP150104645 |
Published version: | http://papers.nips.cc/paper/6608-deep-subspace-clustering-networks |
Appears in Collections: | Aurora harvest 8 Australian Institute for Machine Learning publications Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.