2DPCA Resources

Showing items tagged with "2DPCA"

This repository contains the original author's implementation code for Two-Dimensional PCA (2DPCA) - a novel approach for appearance-based face representation and recognition published in IEEE PAMI. The paper has received 681 citations on Google Scholar. Note: While this algorithm is a classic in pattern recognition (primarily for facial recognition), the "speech recognition" categorization is inaccurate as 2DPCA is not commonly applied to audio processing. The code provides practical implementation of 2DPCA's matrix-based dimensionality reduction technique.

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During the feature extraction phase, multiple algorithms including PCA, 2DPCA, (2D) 2PCA, DiagPCA, and DiagPCA-F-2DPCA were investigated. Unlike vector-based PCA feature extraction, 2DPCA, (2D) ZPCA, DiagPCA, and DiagPCA-I-2DPCA operate directly on image matrices, resulting in significantly lower computational complexity and faster feature extraction speeds than traditional PCA methods. These matrix-based approaches utilize covariance matrices computed directly from 2D image arrays rather than requiring vectorization, making them particularly efficient for high-dimensional image data processing.

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During the feature extraction phase, multiple algorithms including PCA, 2DPCA, (2D) 2PCA, DiagPCA, and DiagPCA-F-2DPCA were investigated. Unlike vector-based PCA feature extraction, 2DPCA, (2D) ZPCA, DiagPCA, and DiagPCA-I-2DPCA operate directly on image matrices, resulting in significantly lower computational complexity and faster feature extraction speeds than traditional PCA methods. These matrix-based approaches utilize covariance matrices computed directly from 2D image arrays rather than requiring vectorization, making them particularly efficient for high-dimensional image data processing.

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A concise MATLAB implementation of 2DPCA (Two-Dimensional Principal Component Analysis) designed for academic projects. Unlike some complex foreign implementations, this version focuses on clarity and practicality. The code includes proper matrix operations for covariance calculation and eigenvalue decomposition, ensuring correctness through rigorous testing. Suitable for beginners learning pattern recognition and experienced users needing a reliable 2DPCA baseline.

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