Unite and Learn: An iterative approach for AI

We highlight the omnipresence of certain linear algebra methods such as the eigenvalue problem or more generally the singular value decomposition in machine learning techniques. An innovative machine learning approach based on Unite and Conquer methods, used in linear algebra, will be presented. In addition to its effectiveness from the point of view of accuracy, the important characteristics of this intrinsically parallel and scalable technique make it well suited to multi-level and heterogeneous parallel and/or distributed architectures. Experimental results, demonstrating the interest of the approach for efficient data analysis in the case of clustering and anomaly detection will be presented.