Collaboration has long been considered an effective approach to learning. However, forming optimal groups can be a time consuming and complex task. Teachers often need to set some constraints for the grouping based on the aim of the collaborative task. To achieve optimal grouping, the formation needs to satisfy these constraints, even when the list of students is unknown. In this research, we i...

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries...

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to ...

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once per group, as opposed to once per variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity o...

The interesting domain of constraint programming has been studied for years. Using Constraint Satisfaction Problem in association with Multi-Agent System has emerged the research in a new field known as Distributed Constraint Satisfaction Problem (DCSP). Many algorithms are proposed to solve DCSP. Inspired from ABT algorithm, we introduce in this paper our algorithm for solving DCSP where we di...

Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose an innovative source localization method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better specify the sparsity profile and to ensure efficient source localization, the proposed approach considers g...