Fisher Algorithm Source Code

One challenge in applying statistical methods to pattern recognition is the dimensionality issue - classification problems are generally simpler in low-dimensional feature spaces than in high-dimensional ones. This leads to dimensionality reduction techniques, where a fundamental approach projects d-dimensional feature space onto a straight line to create one-dimensional space, which is mathematically straightforward. However, the key challenge is ensuring samples remain linearly separable after projection. While linearly separable samples can always find a projection direction maintaining linear separability after dimensionality reduction, Fisher Linear Discriminant specifically determines the optimal projection direction that maximizes separability by maximizing between-class distance while minimizing within-class variance.

Linear Discriminant Analysis (LDA) for Ear Recognition: Methods Comparison and Implementation

Linear Discriminant Analysis (LDA) is a widely-used linear classification method for feature extraction, but its direct application to ear recognition faces dimensionality and small sample size problems. Researchers have developed multiple solutions to address these challenges, implementing various LDA-based ear recognition approaches. This article provides theoretical comparisons and experimental validation of four methods: Fisherears, DLDA, VDLDA, and VDFLDA, with implementation insights and performance analysis demonstrating VDFLDA's superiority.