677 research outputs found
On the Procrustean analogue of individual differences scaling (INDSCAL)
In this paper, individual differences scaling (INDSCAL) is revisited, considering
INDSCAL as being embedded within a hierarchy of individual difference scaling
models. We explore the members of this family, distinguishing (i) models, (ii) the
role of identification and substantive constraints, (iii) criteria for fitting models and (iv) algorithms to optimise the criteria. Model formulations may be based either on data that are in the form of proximities or on configurational matrices. In its configurational version, individual difference scaling may be formulated as a form of generalized Procrustes analysis. Algorithms are introduced for fitting the new
models. An application from sensory evaluation illustrates the performance of the
methods and their solutions
Manifold interpolation and model reduction
One approach to parametric and adaptive model reduction is via the
interpolation of orthogonal bases, subspaces or positive definite system
matrices. In all these cases, the sampled inputs stem from matrix sets that
feature a geometric structure and thus form so-called matrix manifolds. This
work will be featured as a chapter in the upcoming Handbook on Model Order
Reduction (P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W.H.A.
Schilders, L.M. Silveira, eds, to appear on DE GRUYTER) and reviews the
numerical treatment of the most important matrix manifolds that arise in the
context of model reduction. Moreover, the principal approaches to data
interpolation and Taylor-like extrapolation on matrix manifolds are outlined
and complemented by algorithms in pseudo-code.Comment: 37 pages, 4 figures, featured chapter of upcoming "Handbook on Model
Order Reduction
Solving constrained Procrustes problems: a conic optimization approach
Procrustes problems are matrix approximation problems searching for
a~transformation of the given dataset to fit another dataset. They find
applications in numerous areas, such as factor and multivariate analysis,
computer vision, multidimensional scaling or finance. The known methods for
solving Procrustes problems have been designed to handle specific sub-classes,
where the set of feasible solutions has a special structure (e.g. a Stiefel
manifold), and the objective function is defined using a specific matrix norm
(typically the Frobenius norm). We show that a wide class of Procrustes
problems can be formulated and solved as a (rank-constrained) semi-definite
program. This includes balanced and unbalanced (weighted) Procrustes problems,
possibly to a partially specified target, but also oblique, projection or
two-sided Procrustes problems. The proposed approach can handle additional
linear, quadratic, or semi-definite constraints and the objective function
defined using the Frobenius norm but also standard operator norms. The results
are demonstrated on a set of numerical experiments and also on real
applications
Recommended from our members
Factor Analysis of Data Matrices: New Theoretical and Computational Aspects With Applications
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample covariance or correlation matrix with respect to some goodness-of-fit criterion. Predicted factor scores can be obtained as a function of these estimates and the data. In this thesis, the EFA model is considered as a specific data matrix decomposition with fixed unknown matrix parameters. Fitting the EFA model directly to the data yields simultaneous solutions for both loadings and factor scores. Several new algorithms are introduced for the least squares and weighted least squares estimation of all EFA model unknowns. The numerical procedures are based on the singular value decomposition, facilitate the estimation of both common and unique factor scores, and work equally well when the number of variables exceeds the number of available observations.
Like EFA, noisy independent component analysis (ICA) is a technique for reduction of the data dimensionality in which the interrelationships among the observed variables are explained in terms of a much smaller number of latent factors. The key difference between EFA and noisy ICA is that in the latter model the common factors are assumed to be both independent and non-normal. In contrast to EFA, there is no rotational indeterminacy in noisy ICA. In this thesis, noisy ICA is viewed as a method of factor rotation in EFA. Starting from an initial EFA solution, an orthogonal rotation matrix is sought that minimizes the dependence between the common factors. The idea of rotating the scores towards independence is also employed in three-mode factor analysis to analyze data sets having a three-way structure.
The new theoretical and computational aspects contained in this thesis are illustrated by means of several examples with real and artificial data
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