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While recent supervised dictionary learning methods have attained promising results on the classification tasks, their performance depends on the availability of the large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but its relatively easy to acquire a large amount of unlabeled data. In this paper, we propose a probabilistic framework for discriminative dictionary learning which uses both the labeled and unlabeled data. Experimental results demonstrate that the performance of the proposed method is significantly better than the state of the art dictionary based classification methods.

In this paper, a multiple metric learning scheme for human pose estimation from a single image is proposed. Here, we focused on a big challenge of this problem which is; different 3D poses might correspond to similar inputs. To address this ambiguity, some Euclidean distance based approaches use prior knowledge or pose model that can work properly, provided that the model parameters are being estimated accurately. In the proposed method, the manifold of data is divided into several clusters and then, we learn a new metric for each partition by utilizing not only input features, but also their corresponding poses. The manifold clustering allows the decomposition of multiple manifolds into a set of manifolds that are less complex. Furthermore, the input data could be mapped to a new space where the ambiguity problem is minimized. Our guiding principle for learning the distance metrics is to preserve the manifold structure of the input data. The proposed method employs Tikhonov regularization technique to obtain a smooth estimation of the labels. Experiments on the data set of human pose estimation demonstrate that the proposed multiple metric learning consistently outperforms single-metric learning method across different activities by a wide margin.