Hirarchical Harmony Linear Discriminant Analysis

Linear Discriminate Analysis is commonly used in feature reduction. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new way named Hierarchical Harmony Linear Discriminant Analysis (HH-LDA), which computes between class scatter matrixes optimally. It is reached by combining hierarchical scheme and Harmony Search (HS) algorithm. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix causes classes to be more discriminable by transforming data into the new space and consequently, increases the classification accuracy. This transformation matrix is computed through two methods based on linear discrimination. In the first method, we use class dependent LDA to increase classification accuracy by finding a transformation that maximizes the between-class scatter and minimizes within-class scatter using a transformation matrix. Because LDA cannot obtain optimal transformation, in the second method, Harmony Search is used to increase performance of LDA. Obtained results show that utilization of these pre-processing causes increasing the accuracy of different classifiers.