Learning deep and wide: A spectral method for learning deep networks

Ling Shao, Di Wu, Xuelong Li

Research output: Contribution to journalArticlepeer-review

130 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)2303-2308
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Early online date11 Mar 2014
Publication statusPublished - Dec 2014

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