Semantic Pattern Recognition Based on Linear Algebra and Latent Semantic Analysis

Abstract

Pattern recognition is a process of identifying vector of correlated/uncorrelated attributes and discriminate it among other patterns. Pattern recognition is synonymous to machine learning, data mining and Knowledge Discovery in Database (KDD).In this research work we investigate decomposing pattern (i.e., attribute vector) space into subspaces in which patterns cluster around basis of the subspaces. This paper introduces a theory which states that in case of having space of vectors and having basis then Signal Value Decomposition (SVD) can perform excellent in discovering thesis basis, hence, in pattern recognition a space can be decomposed to sub-spaces to reach clustering around basis. Results are collected and discussed and it has proven that SVD and its extension Latent Segment Analysis (LSA) can optimize the process of machine learning and showed a great tendency to converge toward cognitive based recognition.