Explore MATLAB source code curated for "FIsher线性判别" with clean implementations, documentation, and examples.
My comprehensive pattern recognition project includes implementations of Bayes classification, Fisher linear discriminant analysis, PCA and LDA feature extraction, K-means clustering, and Parzen window algorithm. The code is thoroughly commented and comes with test datasets for validation.
Implementation of PCA, Fisher Linear Discriminant, Kernel PCA (KPCA), and 2D Discrete Wavelet Transform (DWT2) for facial recognition systems with dimensionality reduction techniques
Fisher Linear Discriminant serves as one of the most fundamental linear classification algorithms, operating by projecting d-dimensional samples onto an optimal projection line that maximizes class separability. This article implements the Fisher Linear Discriminant algorithm in MATLAB with detailed code explanations, presenting four distinct threshold selection methodologies for practical application.
Fisher Discriminant Analysis for Pattern Recognition - Fisher Linear Discriminant Implementation with MATLAB Code Examples
Pattern Recognition using Linear Classifier and Fisher Linear Discriminant: Classifying Female and Male samples with Training Sets test1 and test2 as Testing Datasets
Overview of frequently used classification algorithms including Nearest Neighbor (NN), K-Means clustering, K-Nearest Neighbors (KNN), and Fisher's Linear Discriminant analysis with implementation insights.
Face Feature Recognition - Key Dimensionality Reduction and Feature Extraction Methods for Computer Vision Applications