Learning Deep and Wide: A Spectral Method for Learning Deep Networks

Shao, Ling, Wu, Di and Li, Xuelong (2014) Learning Deep and Wide: A Spectral Method for Learning Deep Networks. IEEE Transactions on Neural Networks and Learning Systems, 25 (12). pp. 2303-2308. ISSN 2162-237X

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Abstract

Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dimensional embedding explores the complementary property of different views wherein the distribution of each view is sufficiently smooth and hence achieves robustness, given few labeled training data. Our experiments show that spectrally embedding several deep neural networks can explore the optimum output from the multicolumn networks and consistently decrease the error rate compared with a single deep network.

Item Type:	Article
Uncontrolled Keywords:	Deep networks, multispectral embedding, representation learning
Subjects:	G400 Computer Science G700 Artificial Intelligence
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Publisher
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 10:17
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22812

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