MATLAB Implementation of Fisher Discriminant Analysis

This article discusses Fisher Discriminant Analysis, a Fisher Linear Discriminant method used for pattern recognition. The Fisher discriminant method works by calculating the mean vectors and covariance matrices of sample data, enabling the projection of samples into a lower-dimensional space for effective pattern classification and recognition. In MATLAB implementation, this typically involves computing between-class and within-class scatter matrices using functions like mean() and cov(), followed by eigenvalue decomposition to find the optimal projection direction. The method is widely applied in pattern recognition fields as it effectively extracts crucial features from samples and demonstrates excellent classification performance. Key MATLAB functions for implementation include pca() for dimensionality reduction and classify() for classification tasks. Thus, Fisher Discriminant Analysis serves as a powerful tool for solving pattern recognition problems, with MATLAB providing efficient computational methods through its statistical and machine learning toolboxes.