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Microscopic Structure of Bilinear Chemical Data PowerPoint Presentation
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Microscopic Structure of Bilinear Chemical Data

Microscopic Structure of Bilinear Chemical Data

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Microscopic Structure of Bilinear Chemical Data

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  1. 12 Microscopic Structure of Bilinear Chemical Data IASBS, Bahman 2-3, 1392 January 22-23, 2014

  2. 12 Independent Component Analysis (ICA) HadiParastar Sharif University of Technology

  3. Every problem becomes very childish when once it is explained to you. —Sherlock Holmes (The Dancing Men, A.C. Doyle, 1905)

  4. Representation of Multivariate Data • - The key to understand and interpret multivariate data is suitable representation • - Such a representation is achieved using some kind of transform • - Transforms can be linear or non-linear • - Linear transform W applied to a data matrix X with objects as rows and • variables as columns is as follow: • U = WX + E • - Broadly speaking, linear transform can be classified in two groups: • - Second-order methods • - Higher-order methods

  5. Linear Transform Techniques

  6. Soft-modeling methods • Factor Analysis (FA) • Principal Component Analysis (PCA) • Blind source separation (BSS) • Independent Component Analysis (ICA)

  7. hplc.m Simulating HPLC-DAD data

  8. emgpeak.m Chromatograms with distortions

  9. Basic statistics • Expectation • Mean • Correlation matrix

  10. Basic statistics • Covariance matrix • Note

  11. Principal Component Analysis (PCA) • Using an eigenvector rotation, it would be possible to decompose the X matrix into a series of loadings and scores • Underlying or intrinsic factors related to intelligence could then be detected • In chemistry, this approach can be used by diagonalizating the correlation or covariance matrix

  12. Principal component analysis (PCA) Loadings PT Scores Raw data Residuals Data X Model TPT TPT E Noise T = + Explained variance Residual variance X=TPT+E

  13. PCA Model: D = U VT Unexplained variance VT D E = + loadings (projections) U scores D = u1v1T + u2v2T + ……+ unvnT + E n number of components (<< number of variables in D) = + +….+ + D u1v1T u2 v2T unvnT E rank 1 rank 1 rank 1

  14. Principal Component Analysis (PCA) x11 x12 … x114 x2 … x21 x21 x214 • • • • • • • • • • • • • • x1

  15. u11 u2 u12 • • • • u1 • … • • • • • u114 • • • • PCA

  16. x2 x11 x12 … x114 • • • • … x21 x21 x214 • • • • • • • • • • x1 PCA

  17. u11 u21 u2 u1 • • u12 u22 • • … … • • u114 u214 • • • • • • • • PCA u1 = ax1 + bx2 u2 = cx1 + dx2

  18. PCA.m

  19. x1 x2 … = xn 2 x y x x . y = xTy = cos q x . y cos q = y x Inner Product (Dot Product) x . x = xTx = [x1 x2… xn] = x12 + x22 + … +xn2 The cosine of the angle of two vectors is equal to the dot product between the normalized vectors:

  20. x y x x . y = y x y x x . y = - y y x x y = = 1 x . y = 0 Two vectors x and y are orthogonal when their scalar product is zero x . y = 0 and Two vectors x and y are orthonormal

  21. PC2 PCA (Orthogonal coordinate) PC1 ICA (Nonorthogonal coordinate)

  22. Independent Component Analysis: What Is It? • ICA belongs to a class of blind source separation (BSS) methods • The goal of BSS is separating data into underlying informational components, where such data can take the form of spectra, images, sounds, telecommunication channels or stock market prices. • The term “Blind” is intended to imply that such methods can separate data into source signals even if very little is known about the nature of those source signals.

  23. The Principle of ICA: a cocktail-party problem x1(t)=a11 s1(t)+a12 s2(t)+a13 s3(t) x2(t)=a21 s1(t)+a22 s2(t) +a12 s3(t) x3(t)=a31 s1(t)+a32 s2(t) +a33 s3(t)

  24. Independent Component Analysis Herault and Jutten, 1991 • Observed vector x is modelled by a linear latent variable model • Or in matrix form Where: --- The mixing matrix A is constant --- The si are latent variables called the independent components --- Estimate both A and s, observing only x

  25. Independent Component Analysis • ICA bilinear model MCR model PCA model • ICA algorithms try to find independent sources

  26. Independent Component Analysis Model

  27. Basic properties of the ICA model • Must assume: • - The si are independent • - The si are nongaussian • - For simplicity: The matrix A is square • The si defined only up to a mltiplicative constant • The siare not ordered

  28. lCA sources Original sources

  29. Statistical Independence • If two or more signals are statistically independent of each other then the value of one signal provides no information regarding the value of the other signals. • For two variables • For more than two variables • Using expectation operator

  30. Probability Density Function • Moments of probability density functions, which are essentially a form of normalized histograms. PDF Histogram Approximate of PDF

  31. Histogram Probability

  32. Independence and Correlation • The term “correlated” tends to be used in colloquial terms to suggest that two variables are related in a very general sense. • The entire structure of the joint pdf is implicit in the structure of its marginal pdfs because the joint pdf can be reconstructed exactly from the product of its marginal pdfs. Covariance between x and y

  33. Marginal PDF Joint PDF

  34. Independence and Correlation Correlation

  35. Independence and Correlation • The formal similarity between measures of independence and correlation can be interpreted as follows: • Correlation is a measure of the amount of covariation between x and y, and depends on the first moment of the pdf p only. • Independence is a measure of the covariation between [x raised to powers p]and [y raised to powers q], and depends on all moments of the pdfpxy. • Thus, independence can be considered as a generalized measure of correlation , such that

  36. emgpeak.m Chromatograms with distortions