PCA降维 - Matlab Forge | Premium MATLAB Algorithms & Functions

PCA Dimensionality Reduction Code - MATLAB Implementation

This MATLAB implementation performs dimensionality reduction for M*N large datasets with clear procedural steps. The function y = pca(mixedsig) processes an n*T mixed signal matrix where n represents the number of signals and T denotes sampling points, returning an m*T principal component matrix. Algorithm workflow includes: 1) Mean centering, 2) Covariance matrix computation with eigenvalues/eigenvectors, 3) Threshold-based eigenvalue selection, 4) Descending eigenvalue sorting, 5) Small eigenvalue removal, 6) Large eigenvalue removal (typically skipped), 7) Selected eigenvalue merging, 8) Corresponding eigenvector selection, 9) Whitening matrix calculation, 10) Principal component extraction.

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Face Recognition Program Implementing PCA for Dimensionality Reduction

An efficient face recognition program utilizing PCA for dimensionality reduction followed by LDA classification methodology, implemented using the ORL face database with enhanced algorithmic descriptions.

MATLAB 199 views Tagged

PCA Dimensionality Reduction Followed by LDA Classification for Face Recognition

This implementation includes two core modules (PCA and LDA) that perform principal component analysis for dimensionality reduction followed by linear discriminant analysis for classification, achieving excellent experimental results in face recognition applications.

MATLAB 209 views Tagged

Ear Recognition Using Fisher Method with PCA Dimensionality Reduction and LDA Feature Extraction

Implementation of ear recognition using Fisher method incorporating PCA dimensionality reduction and LDA feature extraction techniques

MATLAB 232 views Tagged

Facial Recognition System Using Gabor Wavelet Transform, PCA Dimensionality Reduction, and SVM Multi-Class Classification

This MATLAB program implements a comprehensive facial recognition pipeline that first processes face images using Gabor wavelet transformation for feature extraction, then applies PCA (Principal Component Analysis) for dimensionality reduction, and finally employs an SVM (Support Vector Machine) classifier for multi-class recognition. The implementation supports the complete ORL face database and utilizes the libsvm toolbox (version: libsvm-mat-2.89-3[FarutoUltimate3.0]). Key implementation details include Gabor filter parameter configuration, PCA eigenvector computation, and SVM kernel function selection for optimal multi-class classification performance.

MATLAB 201 views Tagged

PCA Dimensionality Reduction Input Data and Options

PCA dimensionality reduction implementation for pattern recognition, focusing on input data structure and parameter configuration. The data parameter accepts a matrix where each row represents a sample, while the option parameter specifies the target dimensionality for reduction.

MATLAB 211 views Tagged

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PCA降维 Resources

PCA Dimensionality Reduction Code - MATLAB Implementation

Face Recognition Program Implementing PCA for Dimensionality Reduction

PCA Dimensionality Reduction Followed by LDA Classification for Face Recognition

Ear Recognition Using Fisher Method with PCA Dimensionality Reduction and LDA Feature Extraction

Facial Recognition System Using Gabor Wavelet Transform, PCA Dimensionality Reduction, and SVM Multi-Class Classification

PCA Dimensionality Reduction Input Data and Options

Facial Expression Recognition Source Code Utilizing PCA and SVM Functions

PCA-Enhanced Scale-Invariant Feature Transform (SIFT) Algorithm Source Code for Image Object Detection and Recognition

MATLAB Code Implementation of PCA for Feature Dimensionality Reduction

PCA Dimensionality Reduction Followed by LDA Classification for Face Recognition