Some Numerical Results on the Rank of Generic Three-Way Arrays over $\mathbb{R}$

Choulakian, Vartan

doi:10.1137/08073531x

Cited by 4 publications

(2 citation statements)

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“…The grey cells refer to so-called minimal system cases (Choulakian, 2010). By generating random arrays and evaluating roots of a certain polynomial, Choulakian found cases of rank I , and rank higher than I with positive probability.…”

Section: An Overview Of Typical Rank Resultsmentioning

confidence: 99%

Simplicity and Typical Rank Results for Three-Way Arrays

Berge

2011

Psychometrika

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Matrices can be diagonalized by singular vectors or, when they are symmetric, by eigenvectors. Pairs of square matrices often admit simultaneous diagonalization, and always admit block wise simultaneous diagonalization. Generalizing these possibilities to more than two (non-square) matrices leads to methods of simplifying three-way arrays by nonsingular transformations. Such transformations have direct applications in Tucker PCA for three-way arrays, where transforming the core array to simplicity is allowed without loss of fit. Simplifying arrays also facilitates the study of array rank. The typical rank of a three-way array is the smallest number of rank-one arrays that have the array as their sum, when the array is generated by random sampling from a continuous distribution. In some applications, the core array of Tucker PCA is constrained to have a vast majority of zero elements. Both simplicity and typical rank results can be applied to distinguish constrained Tucker PCA models from tautologies. An update of typical rank results over the real number field is given in the form of two tables.Key words: tensor decomposition, tensor rank, typical rank, sparse arrays, Candecomp, Parafac, Tucker component analysis.Three-way data arise when a complete set of measures is obtained for a set of individuals or objects on a set of variables, in a set of circumstances or conditions. For instance, when I subjects are evaluated on J tests on K different occasions, the data consist of an I × J × K three-way array. The array can be cut up into slices: there are K frontal slices of order I × J, J lateral slices of order I × K, and I horizontal slices of order J × K. Tucker (1966), Carroll andChang (1970), and Harshman (1970) have proposed generalizations of principal component analysis (PCA) for the analysis of three-way arrays. For a long time, these methods were well known in psychometrics, but did not receive much attention elsewhere. A few decades ago, however, chemometricians discovered that these methods are particularly useful for their own purposes of data analysis. Nowadays, three-way component analysis is at the heart of what is called tensor decomposition analysis, which has applications in various areas such as signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, graph analysis, neuroscience, and others (Kolda & Brader, 2009). Although tensor decomposition analysis covers multiway arrays beyond the three-way level, only three-way component analysis will be considered in the present paper.Three-way component analysis poses various mathematical problems that have been studied extensively. Among these are the uniqueness properties of three-way decompositions and the transformational freedom that exists when they are absent, the possibilities of transforming arrays to simple forms (with as many vanishing elements as possible, that is), and the rank of three-way arrays. The present paper is focused on the latter two topics. The purpose is to explain simplifying transformations...

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Section: An Overview Of Typical Rank Resultsmentioning

confidence: 99%

Simplicity and Typical Rank Results for Three-Way Arrays

Berge

2011

Psychometrika

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“…A general recipe for solving the system of seven equations in closed form has evaded us. Instead, we used a numerical procedure to estimate a solution based on the Gröbner basis with lexicographic order, see for instance Choulakian [2] and Cox et al ([3], Ch. 2).…”

Section: Using the Gröbner Basis To Solve The System Of Linear Equationsmentioning

confidence: 99%

Direct transformations yielding the knight's move pattern in 3×3×3 arrays

Tendeiro

Berge

Choulakian

2013

Chemometrics and Intelligent Laboratory Systems

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Three-way arrays (or tensors) can be regarded as extensions of the traditional two-way data matrices that have a third dimension. Studying algebraic properties of arrays is relevant, for example, for the Tucker three-way PCA method, which generalizes principal component analysis to three-way data. One important algebraic property of arrays is concerned with the possibility of transformations to simplicity. An array is said to be transformed to a simple form when it can be manipulated by a sequence of invertible operations such that a vast majority of its entries become zero. This paper shows how 3 × 3 × 3 arrays, whether symmetric or nonsymmetric, can be transformed to a simple form with 18 out of its 27 entries equal to zero. We call this simple form the "knight's move pattern" due to a loose resemblance to the moves of a knight in a game of chess. The pattern was examined by Kiers, Ten Berge, and Rocci. It will be shown how the knight's move pattern can be found by means of a numeric-algebraic procedure based on the Gröbner basis. This approach seems to work almost surely for randomly generated arrays, whether symmetric or nonsymmetric.

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