Pervasive Sparsity Modeling for Compressed Image Acquisition – In this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.

In this paper, we take a detailed look at the problem of solving linear optimization problems that require only the problem-specific parameters or no constraints. Our goal is to find a suitable algorithm for each of the above-described data sets, by using the generalization error rate (EER) principle. Using the EER value, we can provide a better estimation of the true EER value and, consequently, estimate a more accurate solution for each problem. In doing this, we consider various possible solutions that are feasible and that cannot be directly generated, and propose and develop a new algorithm based on the technique of approximate optimal policy approximation. Our evaluation shows that it is able to get near the optimal solution, while still has more computational complexities.

Adaptive Canonical Correlation Analysis for Time-Series Prediction and Learning

The Randomized Mixture Model: The Randomized Matrix Model

# Pervasive Sparsity Modeling for Compressed Image Acquisition

Multi-dimensional representation learning for word retrieval

Learning Nonlinear Embeddings from Large and Small Scale Data: An OverviewIn this paper, we take a detailed look at the problem of solving linear optimization problems that require only the problem-specific parameters or no constraints. Our goal is to find a suitable algorithm for each of the above-described data sets, by using the generalization error rate (EER) principle. Using the EER value, we can provide a better estimation of the true EER value and, consequently, estimate a more accurate solution for each problem. In doing this, we consider various possible solutions that are feasible and that cannot be directly generated, and propose and develop a new algorithm based on the technique of approximate optimal policy approximation. Our evaluation shows that it is able to get near the optimal solution, while still has more computational complexities.