Relationships between higher‐order data array configurations and problem formulations in multivariate data analysis
- 1 January 1989
- journal article
- research article
- Published by Wiley in Journal of Chemometrics
- Vol. 3 (1) , 33-48
- https://doi.org/10.1002/cem.1180030106
Abstract
A scaffold for detailed understanding of the concept ‘dimensionality’ in data analysis is furnished by a systematic classification of higher‐order data array configurations. Three major types of problem formulation in multivariate data analysis can be characterized for relevant data classes: data description (intra‐class data structure modelling of inter‐object and inter‐variable relationships) classification (inter‐class discrimination) correlation, regression (inter‐variable relationships). The relationship between these three categories of data analytical problem formulation and the fundamental data array classification is exposed. These relations are augmented to include the general case of data arrays of order R, and R‐way data analysis with the use of bilinear projections is presented. Based upon this, some possible directions for the future development of data analysis may be imagined.Keywords
This publication has 12 references indexed in Scilit:
- Tensorial calibration: II. Second‐order calibrationJournal of Chemometrics, 1988
- Tensorial calibration: I. First‐order calibrationJournal of Chemometrics, 1988
- Analysis of two partial-least-squares algorithms for multivariate calibrationChemometrics and Intelligent Laboratory Systems, 1987
- Soft modelling and chemosystematicsChemometrics and Intelligent Laboratory Systems, 1987
- Multi-way principal components-and PLS-analysisJournal of Chemometrics, 1987
- A theoretical foundation for the PLS algorithmJournal of Chemometrics, 1987
- Principal Component AnalysisPublished by Springer Nature ,1986
- Image analysis and chemical information in imagesAnalytica Chimica Acta, 1986
- Partial least-squares regression: a tutorialAnalytica Chimica Acta, 1986
- Data Analysis: The Need for Models?Journal of the Royal Statistical Society: Series D (The Statistician), 1986