
A correlational measure for an n by p matrix X and an n by q matrix Y assesses their relation without specifying either as a fixed target. This paper discusses a number of useful measures of correlation, with emphasis on measures which are invariant with respect to rotations or changes in singular values of either matrix. The maximization of matrix correlation with respect to transformations XL and YM is discussed where one or both transformations are constrained to be orthogonal. Special attention is focussed on transformations which cause XL and YM to be n by s, where s may be any number between 1 and min (p, q). An efficient algorithm is described for maximizing the correlation between XL and YM where analytic solutions do not exist. A factor analytic example is presented illustrating the advantages of various coefficients and of varying the number of columns of the transformed matrices.
orthogonal transformations, algorithm, Measures of association (correlation, canonical correlation, etc.), factor analysis, maximization of matrix correlation, changes in singular values, rotations, correlation measure, invariance, Applications of statistics to psychology
orthogonal transformations, algorithm, Measures of association (correlation, canonical correlation, etc.), factor analysis, maximization of matrix correlation, changes in singular values, rotations, correlation measure, invariance, Applications of statistics to psychology
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