Add to ORKGThis paper addresses the problem of finding an orthogonal transformation of an arbitrary factor solution that would lead to a least squares fit of a partially specified target matrix. An iterative computing procedure is presented. Key words: orthogonal rotations, factor analysis, least squares, partially specified target, procrustes problem
This paper provides a generalization of the Procrustes problem in which the errors are weighted from...
Component loss functions (CLFs) are used to generalize the quartimax criterion for orthogonal rotati...
factor analysis, quartimax, varimax, orthomax, simultaneous rotation, simultaneous diagonalization, ...
This paper addresses the problem of rotating a factor matrix obliquely to a least squares fit to a ...
The problem of rotating a matrix orthogonally to a best least squares fit with another matrix of the...
This paper provides a generalization of the Procrustes problem in which the errors are weighted from...
tion, target rotation. In their book The Analysis of Intelligence, Guil-ford and Hoepfner (1971, pp....
In this thesis, we present algorithms for local and global minimization of some Procrustes type prob...
A method is offered for orthogonal Procrustes rotation of two or more matrices with missing values, ...
An algorithm is presented for the best least-squares fitting correlation matrix approximating a give...
<p>The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target r...
In the technique developed by K. G. Joreskog to solve the problem for oblique rotation to a specifie...
The VARIMAX rotation for factor analysis is used to orthogonally transform the factor subspace, resu...
Almost all modern rotation of factor loadings is based on optimizing a criterion, for example, the q...
The classical matrix Procrustes problem seeks an orthogonal matrix, U , which most closely transform...
This paper provides a generalization of the Procrustes problem in which the errors are weighted from...
Component loss functions (CLFs) are used to generalize the quartimax criterion for orthogonal rotati...
factor analysis, quartimax, varimax, orthomax, simultaneous rotation, simultaneous diagonalization, ...
This paper addresses the problem of rotating a factor matrix obliquely to a least squares fit to a ...
The problem of rotating a matrix orthogonally to a best least squares fit with another matrix of the...
This paper provides a generalization of the Procrustes problem in which the errors are weighted from...
tion, target rotation. In their book The Analysis of Intelligence, Guil-ford and Hoepfner (1971, pp....
In this thesis, we present algorithms for local and global minimization of some Procrustes type prob...
A method is offered for orthogonal Procrustes rotation of two or more matrices with missing values, ...
An algorithm is presented for the best least-squares fitting correlation matrix approximating a give...
<p>The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target r...
In the technique developed by K. G. Joreskog to solve the problem for oblique rotation to a specifie...
The VARIMAX rotation for factor analysis is used to orthogonally transform the factor subspace, resu...
Almost all modern rotation of factor loadings is based on optimizing a criterion, for example, the q...
The classical matrix Procrustes problem seeks an orthogonal matrix, U , which most closely transform...
This paper provides a generalization of the Procrustes problem in which the errors are weighted from...
Component loss functions (CLFs) are used to generalize the quartimax criterion for orthogonal rotati...
factor analysis, quartimax, varimax, orthomax, simultaneous rotation, simultaneous diagonalization, ...