Shared linear encoder-based Gaussian process latent variable model for visual classification

Multi-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.