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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2019

07.06.2018 | Original Article

Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning

verfasst von: Shudong Hou, Heng Liu, Quansen Sun

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

For multi-view data representation learning, recently the traditional unsupervised CCA method has been converted to supervised ways by introducing label information from samples. However, such supervised CCA variants require large numbers of labeled samples which hampers its practical application. In this paper, in order to mine the most discriminant information only from a few labeled samples, inspired by sparse representation we propose a novel sparse regularized discriminative CCA method to make use of the label information as much as possible. Through constructing sparse weighted matrices in multiple views, we incorporate the structure information into the original CCA framework to extract fused multi-view features which not only are the most correlated but also carry the important discriminative structure information. Our approach is evaluated on both handwritten dataset and face dataset. The experimental results and the comparisons with other related algorithms demonstrate its effectiveness and superiority.
Vorheriger Artikel Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers
Nächster Artikel Sitcom-star-based clothing retrieval for video advertising: a deep learning framework

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Metadaten
Titel
Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning
verfasst von
Shudong Hou
Heng Liu
Quansen Sun
Publikationsdatum
07.06.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3582-2

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