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Supervised subspace learning based on deep randomized networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
Number of pages5
ISBN (Print)9781479999880
Publication statusPublished - 18 May 2016
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing -
Duration: 1 Jan 19001 Jan 2000

Publication series

ISSN (Electronic)2379-190X


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing


In this paper, we propose a supervised subspace learning method that exploits the rich representation power of deep feedforward networks. In order to derive a fast, yet efficient, learning scheme we employ deep randomized neural networks that have been recently shown to provide good compromise between training speed and performance. For optimally determining the learnt subspace, we formulate a regression problem where we employ target vectors designed to encode both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by the network's last hidden layer outputs. We experimentally show that the proposed approach is able to outperform deep randomized neural networks trained by using the standard network target vectors.


  • Deep Neural Networks, Network targets calculation, Supervised Subspace Learning

Publication forum classification

Field of science, Statistics Finland