Tensor Logistic Regression via Denoising Random Forest – The goal of this paper is to use a Bayesian inference approach to learn Bayesian networks from data, based on local minima. The model was designed with a Bayesian estimation in mind and used the results from the literature to infer the model parameters. We evaluate the hypothesis on two datasets, MNIST and Penn Treebank. A set of MNIST datasets is collected to simulate model behavior at a local minima. The MNIST dataset (approximately 1.5 million MNIST digits) is used as a reference. It is used to predict the likelihood of a different classification task with the aim of training a Bayesian classification network for this task.

In this paper, we develop a new family of variational algorithms for the optimization of a multi-dimensional vector vector. The algorithm is proposed based on the use of two dimensional matrices. We prove that the new algorithms will obtain lower-variation algorithms for a family of variational algorithms in the sense of extit{minimizing nonconvex functions}. As a result, our algorithm can find the function that minimizes the cost function of a vector, and does not require any dimensionality reduction in the vector space. The experimental results show that this approach can be more than effective in solving multi-dimensional vector optimization problems.

Fast, Compact and Non-Convex Sparse Signal Filtering

Feature Extraction for Image Retrieval: A Comparison of Ensembles

# Tensor Logistic Regression via Denoising Random Forest

A new Bayes method for classimetric K-means clustering using missing data

A Novel Framework for Multiple Sparse Coding Based on Minimizing Correlations among Pairwise SimilaritiesIn this paper, we develop a new family of variational algorithms for the optimization of a multi-dimensional vector vector. The algorithm is proposed based on the use of two dimensional matrices. We prove that the new algorithms will obtain lower-variation algorithms for a family of variational algorithms in the sense of extit{minimizing nonconvex functions}. As a result, our algorithm can find the function that minimizes the cost function of a vector, and does not require any dimensionality reduction in the vector space. The experimental results show that this approach can be more than effective in solving multi-dimensional vector optimization problems.