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

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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.

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Acknowledgements

This work is partly supported in part by Natural Science Foundation of Anhui Province under Grant 1808085QF210 and Grant 1608085MF129. and in part by the Major and Key Project of Natural Science of Anhui Provincial Department of Education under Grant KJ2015ZD09 and Grant KJ2018A0043.

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University of Technology, Ma’anshan, 243032, People’s Republic of China

    Shudong Hou & Heng Liu

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Quansen Sun

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  1. Shudong Hou
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  2. Heng Liu
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  3. Quansen Sun
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Correspondence to Heng Liu.

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Hou, S., Liu, H. & Sun, Q. Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning. Neural Comput & Applic 31, 7351–7359 (2019). https://doi.org/10.1007/s00521-018-3582-2

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