ECE 595ML Lecture 7.2: Feature Analysis via PCA - Kernal PCA

By Stanley H. Chan

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Researchers should cite this work as follows:

  • Stanley H. Chan (2020), "ECE 595ML Lecture 7.2: Feature Analysis via PCA - Kernal PCA," https://nanohub.org/resources/32434.

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WTHR 200, Purdue University, West Lafayette, IN

ECE 595ML Lecture 7.2: Feature Analysis via PCA - Kernal PCA
  • Lecture 7.2: Kernel-PCAi 1. Lecture 7.2: Kernel-PCAi 0
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  • Outline 2. Outline 12.145478812145479
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  • Motivation of Kernel PCA 3. Motivation of Kernel PCA 25.225225225225227
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  • Kernel for Covariance Matrix 4. Kernel for Covariance Matrix 78.111444778111448
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  • Kernel Trick 5. Kernel Trick 182.81614948281614
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  • Kernel Trick 6. Kernel Trick 246.74674674674677
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  • Eigenvectors of K-PCA 7. Eigenvectors of K-PCA 465.49883216549887
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  • Representation in Kernel Space 8. Representation in Kernel Space 517.917917917918
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  • Example 9. Example 644.61127794461129
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  • Example 10. Example 661.327994661328
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  • Reading List 11. Reading List 695.32866199532873
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