ECE 595ML Lecture 7.1: Feature Analysis via 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 7.1: Feature Analysis via PCA," https://nanohub.org/resources/32433.

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WTHR 200, Purdue University, West Lafayette, IN

ECE 595ML Lecture 7.1: Feature Analysis via PCA
  • Lecture 7.1: Feature Analysis via PCA 1. Lecture 7.1: Feature Analysis … 0
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  • Overview 2. Overview 17.784451117784453
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  • Outline 3. Outline 245.7123790457124
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  • Low-Dimensional Representation 4. Low-Dimensional Representation 306.17283950617286
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  • One Sample Analysis 5. One Sample Analysis 433.93393393393393
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  • One Sample Analysis 6. One Sample Analysis 607.50750750750751
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  • Eigenvalue Problem 7. Eigenvalue Problem 803.2032032032032
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  • Finite Samples 8. Finite Samples 908.57524190857532
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  • Statistical Interpretation 9. Statistical Interpretation 1071.9052385719053
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  • The Eigenface Problem 10. The Eigenface Problem 1203.1698365031698
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  • Low Dimensional Representation 11. Low Dimensional Representation 1248.2482482482483
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  • The Basis Vectors ui 12. The Basis Vectors ui 1393.7937937937938
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  • Representing Faces 13. Representing Faces 1456.4230897564232
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  • Discussion 14. Discussion 1547.7143810477144
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