Visual Explanation of Eigenvalues and Math Process in Latent Semantic Analysis
Yukari Shirota, Basabi Chakraborty · Information Engineering Express · 2016
Latent Semantic Analysis (LSA) is a widely used method in text mining field to extract the underlying concepts in the text document.The mathematical technique behind LSA is Singular Value Decomposition (SVD) in which the key concept is the eigenvalues.It is difficult to understand the underlying mathematics for general people, not proficient in mathematics.One reason might be that the linear algebra textbooks available in the market are not written for nonmathematics majors such as economics students.We believe that there is better teaching method to explain the eigenvalues and eigenvectors to our students.In this paper, we would like to illustrate the method.In the main part of the paper, we have proposed a visualization of the mathematical process behind LSA to make it easily understandable to general people, novice in mathematics.In addition, to understand the SVD process more deeply, another example which is a time series data analysis by SVD is also presented.