Principal Component Analysis decomposition

In the previous section we have developed principal components analysis (PCA) from the fundamental theorem of singular value decomposition (SVD). In particular we have shown by means of eq. (31.1) how an nxp rectangular data matrix X can be decomposed into an nxr orthonormal matrix of row-latent vectors U, a pxr orthonormal matrix of column-latent vectors V and an rxr diagonal matrix of latent values A. Now we focus on the geometrical interpretation of this algebraic decomposition. 

The corresponding decomposition by a principal components analysis gives ... 

We now have the data necessary to calculate the singular value decomposition (SVD) for matrix A. The operation performed in SVD is sometimes referred to as eigenanal-ysis, principal components analysis, or factor analysis. If we perform SVD on the A matrix, the result is three matrices, termed the left singular values (LSV) matrix or the V matrix the singular values matrix (SVM) or the S matrix and the right singular values matrix (RSV) or the V matrix. 

Principal Component Analysis (PCA) is the most popular technique of multivariate analysis used in environmental chemistry and toxicology [313-316]. Both PCA and factor analysis (FA) aim to reduce the dimensionality of a set of data but the approaches to do so are different for the two techniques. Each provides a different insight into the data structure, with PCA concentrating on explaining the diagonal elements of the covariance matrix, while FA the off-diagonal elements [313, 316-319]. Theoretically, PCA corresponds to a mathematical decomposition of the descriptor matrix,X, into means (xk), scores (fia), loadings (pak), and residuals (eik), which can be expressed as... 

Principal component analysis is based on the eigenvalue-eigenvector decomposition of the n h empirical covariance matrix Cy = X X (ref. 22-24). The eigenvalues are denoted by > 2 — Vi > where the last inequality follows from the presence of same random error in the data. Using the eigenvectors u, U2,. . ., un, define the new variables... 

A more detailed decomposition of macromolecular dynamics that can be used not only for assessing convergence but also for other purposes is principal components analysis (PCA), sometimes also called essential dynamics (Wlodek et al. 1997). In PCA the positional covariance matrix C is calculated for a given trajectory after removal of rotational and translational motion, i.e., after best overlaying all structures. Given M snapshots of an N atom macromolecule, C is a 3N X 3A matrix with elements... 

Figure 14.10. Principal component analysis of Py-FI mass spectra of (a) cold and (b) hot water extracts from the sequence of organic litter layers Oi-Oe-Oa in a beech stand (Fagus sylvat-ica) obtained before (-pre) and after (-post) aerobic incubation. The arrows indicate changes due to progressive decomposition top-down in the litter profile. Reprinted from Landgraf, D., Leinweber, P, and Makeschin, F. (2006). Cold and hot water extractable organic matter as indicators of litter decomposition in forest soils. Journal of Plant Nutrition and Soil Science 169,76-82, with permission of Wiley-VCH.

Principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) were applied to the augmented columnwise data matrix D1"1", as shown in Figure 11.16. In both cases, a linear mixture model was assumed to explain the observed data variance using a reduced number of contamination sources. The bilinear data matrix decomposition used in both cases can be written by Equation 11.19 ... 

In organic chemistry, decomposition of molecules into substituents and molecular frameworks is a natural way to characterize molecular structures. In QSAR, both the Hansch-Fujita " and the Free-Wilson classical approaches are based on this decomposition, but only the second one explicitly accounts for the presence or the absence of substituent(s) attached to molecular framework at a certain position. While the multiple linear regression technique was associated with the Free-Wilson method, recent modifications of this approach involve more sophisticated statistical and machine-learning approaches, such as the principal component analysis and neural networks. ... 

Lukovits, I. and Lopata, A. (1980). Decomposition of Pharmacological Activity Indices into Mutually Independent Components Using Principal Component Analysis. J.Med.Chem., 23, 449- 59. 

A general requirement for P-matrix analysis is n = rank(R). Unfortcmately, for most practical cases, the rank of R is greater than the number of components, i.e., rank(R) > n, and rank(R) = min(m, p). Thus, P-matrix analysis is associated with the problem of substituting R with an R that produces rank(R ) = n. This is mostly done by orthogonal decomposition methods, such as principal components analysis, partial least squares (PLS), or continuum regression [4]. Dimension requirements of involved matrices for these methods are m > n, and p > n. If the method of least squares is used, additional constraints on matrix dimensions are needed [4]. The approach of P-matrix analysis does not require quantitative concentration information of all constituents. Specifically, calibration samples with known concentrations of analytes under investigation satisfy the calibration needs. The method of PLS will be used in this chapter for P-matrix analysis. 

Principal Components Analysis (PCA) is a multivariable statistical technique that can extract the strong correlations of a data set through a set of empirical orthogonal functions. Its historic origins may be traced back to the works of Beltrami in Italy (1873) and Jordan in Prance (1874) who independently formulated the singular value decomposition (SVD) of a square matrix. However, the first practical application of PCA may be attributed to Pearson s work in biology [226] following which it became a standard multivariate statistical technique [3, 121, 126, 128]. 

Separating measured data vectors or matrices into independent lower order approximations and residual terms is useful both in process performance evaluation, as variance contributions can be clearly separated, and in feedback process control, as the number of decision variables can be significantly reduced while the adverse effects of autocorrelation are eliminated. In the following two sections orthogonal decomposition approaches using Gram polynomials and principal components analysis (PCA) will be introduced. 

Principal components analysis (PC A) (see Section 3.1) provides a technique to define orthogonal basis functions that are directly constructed from process data, unlike Gram polynomials which are dependent on the data length only. PCA is also uniquely suitable for extracting the dominant features of two-dimensional data like the residual profile obtained after MD/CD decomposition, Yr. 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

Wall, M. E., Rechtsteiner, A., and Rocha, L. M. 2003. Singular value decomposition and principal component analysis. In A Practical Approach to Microarray Data Analysis, (eds. D. R Berrar, W. Dubitzky, M. Granzow), pp. 91-109, Norwell, MA Kluwer. 

In practice, for spectra exploitation, the main procedure is the principal component analysis (PCA), identifying a set of few factors (the first eigenvectors of the matrix), used for the interpretation of data. Then, any spectrum can be explained as a linear combination of these factors (as a decomposition step), the coefficients of which are the PCA scores. 

In principal component analysis (PCA), a matrix is decomposed as a sum of vector products, as shown in Figure 1.6. The vertical vectors (following the object way) are called scores and the horizontal vectors (following the variable way) are called loadings. A similar decomposition is given for three-way arrays. Here, the array is decomposed as a sum of triple products of vectors as in Figure 1.7. This is the PARAFAC model. The vectors, of which there are three different types, are called loadings. 

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