2003
DOI: 10.1002/cem.801
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A new efficient method for determining the number of components in PARAFAC models

Abstract: A new diagnostic called the core consistency diagnostic (CORCONDIA) is suggested for determining the proper number of components for multiway models. It applies especially to the parallel factor analysis (PARAFAC) model, but also to other models that can be considered as restricted Tucker3 models. It is based on scrutinizing the`appropriateness' of the structural model based on the data and the estimated parameters of gradually augmented models. A PARAFAC model (employing dimension-wise combinations of compone… Show more

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Cited by 1,118 publications
(761 citation statements)
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“…The core consistency diagnostic (CORCONDIA), which is an index that measures the degree of trilinearity of the experimental data tensor, was developed by B ro and K iers [48 ]. This index assesses the similarity between the imposed superdiagonal tensor T of ones of the PARAFAC model and the least squares-fitted core tensor G, which is formed by the loading vectors of PARAFAC obtained from a Tucker3 model:…”
Section: Four-w Ay Pa R a Fa C Decompositionmentioning
confidence: 99%
“…The core consistency diagnostic (CORCONDIA), which is an index that measures the degree of trilinearity of the experimental data tensor, was developed by B ro and K iers [48 ]. This index assesses the similarity between the imposed superdiagonal tensor T of ones of the PARAFAC model and the least squares-fitted core tensor G, which is formed by the loading vectors of PARAFAC obtained from a Tucker3 model:…”
Section: Four-w Ay Pa R a Fa C Decompositionmentioning
confidence: 99%
“…The Core Consistency Index, CORCONDIA, has been developed for this purpose in ref. [43]. Its description and some details of its practical use are below.…”
Section: Core Consistency Diagnosticmentioning
confidence: 99%
“…This number can be determined based on several criteria as, for example, visual appearance, explained variance (R 2 ), core consistency diagnostic (CORCONDIA), among others. [32][33][34] In this work, the number of components was assessed on the basis of three criteria: explained variance, CORCONDIA and lack of fit (LOF) of the models.…”
Section: Data Modelingmentioning
confidence: 99%
“…More detailed information can be seen in elsewhere. 33 In complex biological matrices, the determination of the number of factors by CORCONDIA is sometimes elusive and cannot be automated. 35 In this sense, the appropriate number of factors should be evaluated by other criteria.…”
Section: Data Modelingmentioning
confidence: 99%