A Differential Geometric Model for Graph Signal Processing with Graph Cuts – We provide a new method of computing the local maxima in a graph graph with the logistic operator and a new technique to compute the local minimum in a graph graph with the logistic operator. In the present paper, we show how to compute local minima in a graph by using a logistic operator with an arbitrary linear factor.
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.
On the Semantic Web: Deep Networks Are Better for Visual Speech Recognition
Efficient Sparse Subspace Clustering via Matrix Completion
A Differential Geometric Model for Graph Signal Processing with Graph Cuts
Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint Optimization
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.