4-component vector element covariant vectors

Principal component analysis (PCA) is aimed at explaining the covariance structure of multivariate data through a reduction of the whole data set to a smaller number of independent variables. We assume that an m-point sample is represented by the nxm matrix X which collects i=l,...,m observations (measurements) xt of a column-vector x with j=, ...,n elements (e.g., the measurements of n=10 oxide weight percents in m = 50 rocks). Let x be the mean vector and Sx the nxn covariance matrix of this sample... 

Technique 2 Elgenanalysls. It Is well known that the structure of a data set can be uncovered by performing an elgenanalysls of Its covariance matrix.(14) This Is often called principal component analysis. That Is, we arrange the M measurement made on each of N objects as a column vector and combine them to form an M x N matrix, A. A matrix B, resembling the covariance matrix of this data set, Is an M x M matrix AA whose elements are given by... 

Non-Abelian electrodynamics has been presented in considerable detail in a nonrelativistic setting. However, all gauge fields exist in spacetime and thus exhibits Poincare transformation. In flat spacetime these transformations are global symmetries that act to transform the electric and magnetic components of a gauge field into each other. The same is the case for non-Abelian electrodynamics. Further, the electromagnetic vector potential is written according to absorption and emission operators that act on element of a Fock space of states. It is then reasonable to require that the theory be treated in a manifestly Lorentz covariant manner. 

PCA is a statistical technique that has been used ubiquitously in multivariate data analysis." Given a set of input vectors described by partially cross-correlated variables, the PCA will transform them into a set that is described by a smaller number of orthogonal variables, the principle components, without a significant loss in the variance of the data. The principle components correspond to the eigenvectors of the covariance matrix, m, a symmetric matrix that contains the variances of the variables in its diagonal elements and the covariances in its off-diagonal elements (15) ... 

We can arrive at this result from the consideration that the velocity defined by the components dxjdt, dyjdt, dzjdt (or the momentum obtained from this velocity on multiplication by the mass) cannot, in view of Lorentz s transformation, be regarded as a vector, since the differential dt in the denominator is also transformed. We obtain a serviceable covariant definition if we replace dt by dt, where dt is the element of the proper time of the particle, i.e. the time measured in the system of reference in which the particle is at rest. The relation between dt and dt is found by taking the derivative of t y... 

Let n be a vector whose components are random variables, not necessarily independent. Then, the covariance matrix of v, cov(n), has elements... 

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