Classification depend on linear discriminant analysis using desired outputs

Linear Discriminant Analysis (LDA) is a popular technique in pattern recognition. This paper presents a linear discriminant analysis for classification. Firstly, linear discriminant Analysis is introduced. The optimum design procedure is demonstrated in order to calculate the error minimization. Next, linear classifier based on desired outputs is derived to get the desired classification. Also, the performance measure is described and calculated to show the effectiveness of the classification algorithm. The usefulness of the proposed procedures was proved in the simulation results.