1. Chapter 7 Multivariate techniques with text Parallel embedded system design lab 이청용

2. 7.1 Introduction Data collecting by taking measurements often unpredictable Measurement error Randomly selected objects Multivariate statistics Techniques for analyzing simultaneously text mining Principal components analysis

3. 7.2 Basic statistics Sample variance Z Scores Applied to Poe Computing how a data value compares to a data set Converting value dimensionless

4. 7.2 Basic statistics

6. Z-score of 4for “The Forest Reverie”

7. 7.2.2 Word correlations among Poe’s short stories Z-score has problem about comparing between units Correlation 7.2 Basic statistics

12. 7.2.3 Correlations and cosines Computing cosine using matrix multiplication

13. 7.2 Basic statistics 7.2.3 Correlations and cosines

14. 7.2 Basic statistics 7.2.4 Correlations and covariances Covariance Correlation

15. 7.3 Basic linear algebra Square matrix X, M (having at least one nonzero vector) Satisfying n by n correlation and covariance matrix n real, orthogonal eigenvectors with n real eigenvalues λ = number

16. 7.3 Basic linear algebra 7.3.1 2 by 2 correlation matrices

17. 7.3 Basic linear algebra

18. 7.3.1 2 by 2 correlation matrices 7.3 Basic linear algebra

19. 7.3.1 2 by 2 correlation matrices 7.3 Basic linear algebra

20. First : linear function of the original Second : vector C has unit length Third : each pair of and (i ≠ j) Four : the variances of,, …, are ordered from largest to smallest 7.4 Principal components analysis

21. 7.4.1 Finding the principal components Correlation matrix z-score Covariance matrix original data values 7.4 Principal components analysis

23. Computing the principal components with function procmp() 7.4 Principal components analysis

24. Using summary() on the output of prcomp() 7.4 Principal components analysis

25. Computing the principal components using the covariance matrix 7.4 Principal components analysis

26. Another PCA example with Poe’s short stories 7.4 Principal components analysis

27. Another PCA example with Poe’s short stories 7.4 Principal components analysis

28. 7.4.4 Rotations Only changing orientation but not the shape of any object Any rotation in n-dimensions is representable by an n-by-n matrix A PCA preserves all of the information