Towards deeper match for multi-view oriented multiple kernel learning

doi:10.1016/j.patcog.2022.109119

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Towards deeper match for multi-view oriented multiple kernel learning

2023-02-01

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Wenzhu Yan

Yanmeng Li

Ming–Hsuan Yang *

DOI：10.1016/j.patcog.2022.109119

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Abstract

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Multi-view representation learning aims to exploit the complementary information underlying multiple view data to enhance the expressive power of data representation. Given that kernels in multiple ker-nel learning naturally correspond to different views, previous shallow similarity learning models cannot fully capture the complex hierarchical information. This work presents an effective deeper match model for multi-view oriented kernel (DMMV) learning which brings a deeper insight into the kernel match for similarity based multi-view representation fusion. Specifically, we propose local deep view-specific self-kernel (LDSvK) by mimicking the deep neural networks to faithfully characterize the local similarity between view-specific samples. Thus, the representation capacity of each view can be saliently analyzed. We build the global deep multi-view fusion kernel (GDMvK) by learning deep fusion of LDSvKs to learn a comprehensive measurement of the cross-view similarity. Notably, the proposed learning framework of the deeper local information extraction and global deep multiple kernel fusion provides a robust way in fitting multi-view data, and yields better learning performance. Experimental results on several multi -view benchmark datasets well demonstrate the effectiveness of our DMMV over other state-of-the-art methods.(c) 2022 Elsevier Ltd. All rights reserved.

Keywords:

Multi-view representation

Deep kernel

Feature fusion

Classification