Nested granular local learning

Local learning approaches are especially easy for parallel processing, so they are very important for cloud computing. In 1997, Lotfi A. Zadeh proposed the concept of Granular Computing (GrC). Zadeh proposed that there are three basic concepts that underlie human cognition: granulation, organization and causation and a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. In this paper, we give out a novel local learning approach based on the concept of Granular computing named as “nested local learning NGLL”. The experiment shows that the novel NGLL approach is better than the probabilistic latent semantic analysis (PLSA).