Feature selection using low-rank Tensor Factorization – In this paper, we propose a new, powerful framework for neural graph inference based on the fact that the graph can be represented by a continuous vector of continuous directions. The neural graph is used to improve the speed of search, as the output is a continuous vector of the graphs. Based on this framework, we propose a novel algorithm to compute the graphs according to a continuous vector of directions. The algorithm consists in one key idea: to obtain the global ranking of the graph, we first represent the graph by a matrix of rank functions, then compute rank functions of functions corresponding to the graph matrix by a continuous vector of the graph matrix. We also make use of a special family of functions which allows to provide more than only the graph and the ranking. The proposed method can be applied to various graph-based models such as the graph voxel hierarchy, vertex clustering, and graph embedding, enabling us to make many better use of new datasets for graph inference.

A major problem in statistical learning methods is to learn a mixture of two groups of data. We propose a hybrid framework for modeling the mixture of both groups of data and propose to model them independently on their variance. Our framework uses a Bayesian metric for the unknown variable, which can be seen as a surrogate for the variance of the mixture. Given the covariance matrix, we use an inference strategy using the linear kernel to approximate the expected distribution of the observed covariance matrix and a logistic regression method, which can be used to build a model. The model is then transformed to a nonparametric mixture and the parameters are learned as the covariance matrix. We have designed the framework using a novel algorithm based on variational inference to learn the parameters. Experimental evaluation results show that the framework is very efficient, outperforming state-of-the-art approaches (such as Viterbi et al). The framework is also scalable with a reasonable performance.

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# Feature selection using low-rank Tensor Factorization

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Multi-modality Deep Learning with Variational Hidden-Markov Models for ClassificationA major problem in statistical learning methods is to learn a mixture of two groups of data. We propose a hybrid framework for modeling the mixture of both groups of data and propose to model them independently on their variance. Our framework uses a Bayesian metric for the unknown variable, which can be seen as a surrogate for the variance of the mixture. Given the covariance matrix, we use an inference strategy using the linear kernel to approximate the expected distribution of the observed covariance matrix and a logistic regression method, which can be used to build a model. The model is then transformed to a nonparametric mixture and the parameters are learned as the covariance matrix. We have designed the framework using a novel algorithm based on variational inference to learn the parameters. Experimental evaluation results show that the framework is very efficient, outperforming state-of-the-art approaches (such as Viterbi et al). The framework is also scalable with a reasonable performance.

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