- Category:
- matlab
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- File Size:
- 1kb
- Update:
- 2019-06-19
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- Uploaded by:
- Jerk_zhu
Description: In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using kernel function technology. The linear operation of the original PCA is performed in a replicated kernel Hilbert space using a kernel function. The operation step potential of KPCA is to transform the data into kernels before PCA, and then calculate the correlation coefficient matrix.
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| Filename | Size | Date |
|---|
| 核函数主成分分析KPCA\主成分分析PCA.m | 301 | 2018-11-18
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| 核函数主成分分析KPCA\核函数kernel.m | 241 | 2018-11-21
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| 核函数主成分分析KPCA\核函数主成分分析kPCA.m | 617 | 2018-11-22
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| 核函数主成分分析KPCA | 0 | 2019-06-19 |
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