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PCA (Principal Component Analysis)

PCA (Principal Component Analysis). Training. Prof. Seewhy Lee Presents. 1. PCA 2. Example 3. Homework. 1. P CA. Eigenvalue, Eigenvector. Principal Component Analysis. 2 . Example. Given Data. Make Zero Mean. Correlation Matrix. Eigenvalues & Eigenvectors. In Two Dim.

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PCA (Principal Component Analysis)

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  1. PCA (Principal Component Analysis) Training Prof. Seewhy Lee Presents

  2. 1. PCA 2. Example 3. Homework

  3. 1. PCA

  4. Eigenvalue, Eigenvector

  5. Principal Component Analysis

  6. 2. Example

  7. Given Data

  8. Make Zero Mean

  9. Correlation Matrix

  10. Eigenvalues & Eigenvectors

  11. In Two Dim.

  12. Data Transformation

  13. Result

  14. 3. Homework

  15. 열 개 이상의 데이터를 X 비슷한 모양이 되도록 배치한다. 이것이 N개의 x 벡터이다. • 평균을 계산하여 x 벡터에서 뺀다. N개의 y(=x-μ) 벡터이다.

  16. SUMSQ, SUMPRODUCT 함수 이용하여 Correlation Matrix를 계산한다. • 두 Eigenvalue를 구한다. 복잡하므로 조심조심 ㅋ • Eigenvector를 구한다. 이것은 아직 규격화되지 않은 상태.

  17. Eigenvector v의 크기를 구한 다음 규격화한 것이 Eigenvector q이다. • 규격화된 두 아이겐벡터가 변환행렬 Q가 된다. 성분 배치에 주의 Eigenvector 1 Eigenvector 2

  18. 행렬 곱 명령어 mmult 이용하여 벡터 y를 Q로 변환한다. z=Qy. • z를 그래프로 그린다. • 학번_성명.xlsx 파일을 e-Class에제출

  19. PCA Can you feel the usefulness?

  20. Thanks! Prof. Seewhy Lee

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