Bayesian Tensor Approach for 3-D Face Modeling
ABSTRACT Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA.
Article: Proximal support tensor machines[Show abstract] [Hide abstract]
ABSTRACT: To utilize the structural information present in multidimensional features of an object, a tensor-based learning framework, termed as support tensor machines (STMs), was developed on the lines of support vector machines. In order to improve it further we have developed a least squares variant of STM, termed as proximal support tensor machine (PSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of PSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in simulations over face detection and handwriting recognition datasets.International Journal of Machine Learning and Cybernetics. 12/2013;
Conference Paper: Latent Tensor Transfer Learning for RGB-D Action RecognitionACM Multimedia; 01/2014
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ABSTRACT: The input data of collaborative filtering, also known as recommendation system, are usually sparse and noisy. In addition, in many cases the data are time-variant and have obvious periodic property. In this paper, we take the two characteristics into account. To utilize the time-variant and periodic properties, we describe the data as a three-order tensor and then formulate the collaborative filtering as a problem of probabilistic tensor decomposition with a time-periodical constraint. The robustness is achieved by employing Tsallis divergence to describe the objective function and q-EM algorithm to find the optimal solution. The proposed method is demonstrated on movie recommendation. Experimental results on two Netflix and Movielens databases show the superiority of the proposed method.Neurocomputing. 11/2013; 119:139-143.