Shared linear encoder-based Gaussian process latent variable model for visual classification
Li J.4; Lu G.3; Zhang B.2; Zhang D.1
Source PublicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
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
URLView the original
Fulltext Access
Citation statistics
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.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li J.]'s Articles
[Lu G.]'s Articles
[Zhang B.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li J.]'s Articles
[Lu G.]'s Articles
[Zhang B.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li J.]'s Articles
[Lu G.]'s Articles
[Zhang B.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.