UM
Shared linear encoder-based Gaussian process latent variable model for visual classification
Li J.4; Lu G.3; Zhang B.2; Zhang D.1
2018-10-15
Source PublicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
Pages26-34
AbstractMulti-view learning has shown its powerful potential in many applications and achieved outstanding performances compared with the single-view based methods. In this paper, we propose a novel multi-view learning model based on the Gaussian Process Latent Variable Model (GPLVM) to learn a shared latent variable in the manifold space with a linear and gaussian process prior based back projection. Different from existing GPLVM methods which only consider a mapping from the latent space to the observed space, the proposed method simultaneously takes a back projection from the observation to the latent variable into account. Concretely, due to the various dimensions of different views, a projection for each view is first learned to linearly map its observation to a subspace. The gaussian process prior is then imposed on another transformation to non-linearly and efficiently map the learned subspace to a shared manifold space. In order to apply the proposed approach to the classification, a discriminative regularization is also embedded to exploit the label information. Experimental results on three real-world databases substantiate the effectiveness and superiority of the proposed approach as compared with several state-of-the-art approaches.
KeywordClassification Gaussian process Latent variable Multi-view
DOI10.1145/3240508.3240520
URLView the original
Language英語
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.The Chinese University of Hong Kong, Shenzhen
2.Universidade de Macau
3.Harbin Institute of Technology
4.Hong Kong Polytechnic University
Recommended Citation
GB/T 7714
Li J.,Lu G.,Zhang B.,et al. Shared linear encoder-based Gaussian process latent variable model for visual classification[C],2018:26-34.
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