Improving the speed of multiway algorithms part II: Compression

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Standard

Improving the speed of multiway algorithms part II : Compression. / Bro, Rasmus; Andersson, Claus A.

In: Chemometrics and Intelligent Laboratory Systems, Vol. 42, No. 1-2, 24.08.1998, p. 105-113.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bro, R & Andersson, CA 1998, 'Improving the speed of multiway algorithms part II: Compression', Chemometrics and Intelligent Laboratory Systems, vol. 42, no. 1-2, pp. 105-113. https://doi.org/10.1016/S0169-7439(98)00011-2

APA

Bro, R., & Andersson, C. A. (1998). Improving the speed of multiway algorithms part II: Compression. Chemometrics and Intelligent Laboratory Systems, 42(1-2), 105-113. https://doi.org/10.1016/S0169-7439(98)00011-2

Vancouver

Bro R, Andersson CA. Improving the speed of multiway algorithms part II: Compression. Chemometrics and Intelligent Laboratory Systems. 1998 Aug 24;42(1-2):105-113. https://doi.org/10.1016/S0169-7439(98)00011-2

Author

Bro, Rasmus ; Andersson, Claus A. / Improving the speed of multiway algorithms part II : Compression. In: Chemometrics and Intelligent Laboratory Systems. 1998 ; Vol. 42, No. 1-2. pp. 105-113.

Bibtex

@article{02092d249b30402a8c305ca074487d58,
title = "Improving the speed of multiway algorithms part II: Compression",
abstract = "In this paper an approach is developed for compressing a multiway array prior to estimating a multilinear model with the purpose of speeding up the estimation. A method is developed which seems very well-suited for a rich variety of models with optional constraints on the factors. It is based on three key aspects: (1) a fast implementation of a Tucker3 algorithm, which serves as the compression method, (2) the optimality theorem of the CANDELINC model, which ensures that the compressed array preserves the original variation maximally, and (3) a set of guidelines for how to incorporate optional constraints. The compression approach is tested on two large data sets and shown to speed up the estimation of the model up to 40 times. The developed algorithms can be downloaded from http:\\newton.mli.kvl.dk\foodtech.html.",
keywords = "CANDELINC, Constraints, Data compression, PARAFAC, Tucker1, Tucker3",
author = "Rasmus Bro and Andersson, {Claus A.}",
year = "1998",
month = aug,
day = "24",
doi = "10.1016/S0169-7439(98)00011-2",
language = "English",
volume = "42",
pages = "105--113",
journal = "Chemometrics and Intelligent Laboratory Systems",
issn = "0169-7439",
publisher = "Elsevier",
number = "1-2",

}

RIS

TY - JOUR

T1 - Improving the speed of multiway algorithms part II

T2 - Compression

AU - Bro, Rasmus

AU - Andersson, Claus A.

PY - 1998/8/24

Y1 - 1998/8/24

N2 - In this paper an approach is developed for compressing a multiway array prior to estimating a multilinear model with the purpose of speeding up the estimation. A method is developed which seems very well-suited for a rich variety of models with optional constraints on the factors. It is based on three key aspects: (1) a fast implementation of a Tucker3 algorithm, which serves as the compression method, (2) the optimality theorem of the CANDELINC model, which ensures that the compressed array preserves the original variation maximally, and (3) a set of guidelines for how to incorporate optional constraints. The compression approach is tested on two large data sets and shown to speed up the estimation of the model up to 40 times. The developed algorithms can be downloaded from http:\\newton.mli.kvl.dk\foodtech.html.

AB - In this paper an approach is developed for compressing a multiway array prior to estimating a multilinear model with the purpose of speeding up the estimation. A method is developed which seems very well-suited for a rich variety of models with optional constraints on the factors. It is based on three key aspects: (1) a fast implementation of a Tucker3 algorithm, which serves as the compression method, (2) the optimality theorem of the CANDELINC model, which ensures that the compressed array preserves the original variation maximally, and (3) a set of guidelines for how to incorporate optional constraints. The compression approach is tested on two large data sets and shown to speed up the estimation of the model up to 40 times. The developed algorithms can be downloaded from http:\\newton.mli.kvl.dk\foodtech.html.

KW - CANDELINC

KW - Constraints

KW - Data compression

KW - PARAFAC

KW - Tucker1

KW - Tucker3

UR - http://www.scopus.com/inward/record.url?scp=0032563557&partnerID=8YFLogxK

U2 - 10.1016/S0169-7439(98)00011-2

DO - 10.1016/S0169-7439(98)00011-2

M3 - Journal article

AN - SCOPUS:0032563557

VL - 42

SP - 105

EP - 113

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

IS - 1-2

ER -

ID: 222926396