Kernel PCA and Pre-Image Reconstruction
Overview
In this package, we implement standard PCA, kernel PCA, and pre-image reconstruction of Gaussian kernel PCA.
We also provide three demos:
- Two concentric spheres embedding;
- Face classification with PCA/kPCA;
- Active shape models with kPCA.
Standard PCA is not optimized for very high dimensional data. But our kernel PCA implementation is very efficient, and has been used in many research projects.
This library is also available at MathWorks:
Citations
If you use this library, please cite:
@article{wang2012kernel,
title={Kernel principal component analysis and its applications in face recognition and active shape models},
author={Wang, Quan},
journal={arXiv preprint arXiv:1207.3538},
year={2012}
}
인용 양식
Quan Wang (2024). Kernel PCA and Pre-Image Reconstruction (https://github.com/wq2012/kPCA/releases/tag/v3.2), GitHub. 검색됨 .
MATLAB 릴리스 호환 정보
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
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demo1
demo2
demo3
버전 | 게시됨 | 릴리스 정보 | |
---|---|---|---|
3.2 | See release notes for this release on GitHub: https://github.com/wq2012/kPCA/releases/tag/v3.2 |
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1.4.0.0 | Fixed a fatal bug in pre-image reconstruction. |
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1.3.0.0 | addpath('../code') in demo2 |
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1.2.0.0 | We replaces all demos, and the data used for the demo. We also updated the document to provide better illustration and better experiments. Now the code generates exactly the same results as shown in the paper. |
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1.1.0.0 | The efficiency is optimized. |
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1.0.0.0 |