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Supervised subspace learning based on deep randomized networks

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
KustantajaThe Institute of Electrical and Electronics Engineers, Inc.
Sivut2584-2588
Sivumäärä5
ISBN (painettu)9781479999880
DOI - pysyväislinkit
TilaJulkaistu - 18 toukokuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING -
Kesto: 1 tammikuuta 19001 tammikuuta 2000

Julkaisusarja

Nimi
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING
Ajanjakso1/01/001/01/00

Tiivistelmä

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.

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