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Multilinear Principal Component Analysis
Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that th ... (PCA) that is used to analyze M-way arrays, also informally referred to as "data tensors". M-way arrays may be modeled by linear tensor models, such as CANDECOMP/Parafac, or by multilinear tensor models, such as multilinear principal component analysis (MPCA) or multilinear independent component analysis (MICA). Tensor rank decomposition were introduced by Frank Lauren Hitchcock in 1927; explanded upon with the Tucker decomposition; and by the "3-mode PCA" by KroonenbergP. M. Kroonenberg and J. de LeeuwPrincipal component analysis of three-mode data by means of alternating least squares algorithms Psychometrika, 45 (1980), ...
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Multilinear Algebra
Multilinear algebra is the study of Function (mathematics), functions with multiple vector space, vector-valued Argument of a function, arguments, with the functions being Linear map, linear maps with respect to each argument. It involves concepts such as Matrix (mathematics), matrices, tensors, multivectors, System of linear equations, systems of linear equations, Higher-dimensional space, higher-dimensional spaces, Determinant, determinants, inner product, inner and outer product, outer products, and Dual space, dual spaces. It is a mathematical tool used in engineering, machine learning, physics, and mathematics. Origin While many theoretical concepts and applications involve Vector space, single vectors, mathematicians such as Hermann Grassmann considered structures involving pairs, triplets, and multivectors that generalize Vector (mathematics and physics), vectors. With multiple combinational possibilities, the space of multivectors expands to 2''n'' dimensions, where ''n'' ...
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Principal Component Analysis
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where the i-th vector is the direction of a line that best fits the data while being orthogonal to the first i-1 vectors. Here, a best-fitting line is defined as one that minimizes the average squared perpendicular distance from the points to the line. These directions (i.e., principal components) constitute an orthonormal basis in which different individual dimensions of the data are linearly uncorrelated. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identi ...
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Tensor Rank Decomposition
In multilinear algebra, the tensor rank decomposition or rank-''R'' decomposition is the decomposition of a tensor as a sum of ''R'' rank-1 tensors, where ''R'' is minimal. Computing this decomposition is an open problem. Canonical polyadic decomposition (CPD) is a variant of the tensor rank decomposition, in which the tensor is approximated as a sum of ''K'' rank-1 tensors for a user-specified ''K''. The CP decomposition has found some applications in linguistics and chemometrics. It was introduced by Frank Lauren Hitchcock in 1927 and later rediscovered several times, notably in psychometrics. The CP decomposition is referred to as CANDECOMP, PARAFAC, or CANDECOMP/PARAFAC (CP). Note that the PARAFAC2 rank decomposition is a variation of the CP decomposition. Another popular generalization of the matrix SVD known as the higher-order singular value decomposition computes orthonormal mode matrices and has found applications in econometrics, signal processing, computer vision, c ...
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Frank Lauren Hitchcock
Frank Lauren Hitchcock (March 6, 1875 – May 31, 1957) was an American mathematician and physicist known for his formulation of the transportation problem in 1941. Academic life Frank did his preparatory study at Phillips Andover Academy. He entered Harvard University and completed his bachelor's degree in 1896. Then he began teaching, first in Paris and at Kenyon College in Gambier, Ohio. From 1904 to 1906 he taught chemistry at North Dakota State University, Fargo. Hitchcock returned to Massachusetts and began to teach at Massachusetts Institute of Technology and study at the graduate level at Harvard. In 1910 he obtained a Ph.D. with a thesis entitled, ''Vector Functions of a Point.'' Hitchcock stayed at MIT until retirement, publishing his analysis of optimal distribution in 1941. Personal life Frank Hitchcock was descended from New England forebears. His mother was Susan Ida Porter (b. January 1, 1848, Middlebury, Vermont) and his father was Elisha Pike Hitchcock. Hi ...
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Journal Of Mathematics And Physics
The journal ''Studies in Applied Mathematics'' is published by Wiley–Blackwell on behalf of the Massachusetts Institute of Technology. It features scholarly articles on mathematical applications in allied fields, notably computer science, mechanics, astrophysics, geophysics, biophysics, and high-energy physics. Its pedigree came from the ''Journal of Mathematics and Physics'' which was founded by the MIT Mathematics Department in 1920. The Journal changed to its present name in 1969. The journal was edited from 1969 by David Benney of the Department of Mathematics, Massachusetts Institute of Technology. According to ISI Journal Citation Reports ''Journal Citation Reports'' (''JCR'') is an annual publication by Clarivate. It has been integrated with the Web of Science and is accessed from the Web of Science Core Collection. It provides information about academic journals in the natur ..., in 2020, it ranked 26th among the 265 journals in the Applied Mathematics ...
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Tucker Decomposition
In mathematics, Tucker decomposition decomposes a tensor into a set of matrices and one small core tensor. It is named after Ledyard R. Tucker although it goes back to Hitchcock in 1927. Initially described as a three-mode extension of factor analysis and principal component analysis it may actually be generalized to higher mode analysis, which is also called higher-order singular value decomposition (HOSVD) or the M-mode SVD. The algorithm to which the literature typically refers when discussing the Tucker decomposition or the HOSVD is the M-mode SVD algorithm introduced by Vasilescu and Terzopoulos, but misattributed to Tucker or De Lathauwer etal. It may be regarded as a more flexible PARAFAC (parallel factor analysis) model. In PARAFAC the core tensor is restricted to be "diagonal". In practice, Tucker decomposition is used as a modelling tool. For instance, it is used to model three-way (or higher way) data by means of relatively small numbers of components for each o ...
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Psychometrika
''Psychometrika'' is the official journal of the Psychometric Society, a professional body dedicated to psychometrics and quantitative psychology. The journal focuses on quantitative methods for the measurement and evaluation of human behavior, including statistical methods and other mathematical techniques. Past editors include Marion Richardson, Dorothy Adkins, Norman Cliff, and Willem J. Heiser. According to ''Journal Citation Reports'', the journal had an impact factor of 2.9 in 2023. History In 1935, LL Thurstone, EL Thorndike and JP Guilford founded ''Psychometrika'' and also the Psychometric Society. Editors-in-chief The current editor of the journal is Sandip Sinharay of Educational Testing Service. The complete list of editor-in-chief of Psychometrika can be found at: https://www.psychometricsociety.org/content/past-psychometrika-editors The following is a subset of persons who have been editor-in-chief of Psychometrika: * Paul Horst * Albert K. Kurtz * Dorot ...
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Multilinear Singular Value Decomposition
Multilinear may refer to: * Multilinear form, a type of mathematical function from a vector space to the underlying field * Multilinear map, a type of mathematical function between vector spaces * Multilinear algebra Multilinear algebra is the study of Function (mathematics), functions with multiple vector space, vector-valued Argument of a function, arguments, with the functions being Linear map, linear maps with respect to each argument. It involves concept ...
, a field of mathematics {{mathdab ...
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HOSVD
In multilinear algebra, the higher-order singular value decomposition (HOSVD) of a tensor is a specific orthogonal Tucker decomposition. It may be regarded as one type of generalization of the matrix singular value decomposition. It has applications in computer vision, computer graphics, machine learning, scientific computing, and signal processing. Some aspects can be traced as far back as F. L. Hitchcock in 1928, but it was L. R. Tucker who developed for third-order tensors the general Tucker decomposition in the 1960s, further advocated by L. De Lathauwer ''et al.'' , or advocated by Vasilescu and Terzopoulos. Although the term HOSVD was coined by De Lathauwer, the algorithm most commonly referred to as the Tucker or Higher-Order Singular Value Decomposition (HOSVD) in the literature was originally introduced by Vasilescu and Terzopoulos under the name M-mode SVD.M. A. O. Vasilescu, D. Terzopoulos (2002), "Multilinear Analysis of Image Ensembles: TensorFaces," Proc. 7th Euro ...
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