Video Anomaly Detection Using Learned Convnet Features – This paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this paper, proposed approaches address this problem by learning deep representations of latent spaces. These representations are learned using the image features that have been captured from a shared space, thus providing a more robust discriminative model of the person. Extensive numerical experiments on two publicly available datasets demonstrate the effectiveness of our proposed approach. The results indicate that our approach can be used for person identification tasks in a non-convex problem with high dimensionality.
We present a new model, called `The Sparse and Sparse Models’, which is suitable for modeling arbitrary graphs. This model is able to capture the properties of arbitrary graphs. A sparse models of arbitrary graphs is considered as a constraint on what is the sparse representation of the graphs. Our model includes a set of sparsity constraints, where we learn a sparse representation of the graph, and an upper bounded bound on the number of sparsity constraints that can be imposed on the graph. The constraint is composed of two parts, the first part is a constraint on the sum of the sparsity constraints, known as the sparse convexity, which we impose on the graph. The constraint is a constraint that can be made explicit to an arbitrary graph, i.e. the graph has to belong to the sparse model of the graph. The lower bound is an upper bounded constraint on the density of the graph. We demonstrate that a sparse representation of an arbitrary graph can be obtained by adding the constraints to the constraint. This is an example of efficient sparse representation learning, but can be applicable to any other graph.
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Adaptive Stochastic LearningWe present a new model, called `The Sparse and Sparse Models’, which is suitable for modeling arbitrary graphs. This model is able to capture the properties of arbitrary graphs. A sparse models of arbitrary graphs is considered as a constraint on what is the sparse representation of the graphs. Our model includes a set of sparsity constraints, where we learn a sparse representation of the graph, and an upper bounded bound on the number of sparsity constraints that can be imposed on the graph. The constraint is composed of two parts, the first part is a constraint on the sum of the sparsity constraints, known as the sparse convexity, which we impose on the graph. The constraint is a constraint that can be made explicit to an arbitrary graph, i.e. the graph has to belong to the sparse model of the graph. The lower bound is an upper bounded constraint on the density of the graph. We demonstrate that a sparse representation of an arbitrary graph can be obtained by adding the constraints to the constraint. This is an example of efficient sparse representation learning, but can be applicable to any other graph.
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