Link prediction is an important task for analysing relational data such as the friendship relation on a social networking website that also has applications in other domains like, information retrieval, bioinformatics and e-commerce. The problem of link prediction is to predict the existence or absence of edges between vertices of a network. In this paper, we present a novel non-parametric latent feature relational model based on distance dependent Indian buffet process (DDIBP), by which we can utilise the information of topological structure of the network such as shortest path and connectivity of the nodes and incorporate them into the proposed Bayesian Non-parametric latent feature model which can automatically infer the unknown latent feature dimension. We also develop an efficient MCMC algorithm to compute the posterior distribution of the hidden variables with a highly nonlinear link likelihood function. Experimental results on four real datasets demonstrate the superiority of the proposed method over other latent feature models for link prediction problem.

In this paper, we present a new method to solve the object tracking problem in video sequences based on the combination of sparse representation and Gaussian process. Most of sparse representation based trackers only consider the holistic representation and do not make full use of motion information of the target, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust probabilistic tracking model in which the motion information of the target object in the previous frames (this information is captured by Gaussian process) is used to define a prior distribution on the object location in the current frame. Then, by using an appropriate likelihood distribution model (this is done via sparse representation), we can compute the posterior distribution of the object location on the current frame. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed object tracking algorithm.

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases of the pose dictionary. Two strategies for dictionary construction are presented: (i) constructing the dictionary by randomly selecting the frames of a sequence and (ii) selecting specific frames of a sequence as dictionary atoms. We analyzed the effect of each strategy on the accuracy of pose estimation. Extensive experiments on datasets of various human activities show that our proposed method outperforms state-of-the-art methods.