Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/918388
- Title
- Two-way data analysis: evolving factor analysis
- Author/Creator
-
Maeder, M.;
De Juan, A.
- Institution
- The University of Newcastle. Faculty of Science & Information Technology, School of Environmental and Life Sciences
- Description
- Evolving factor analysis (EFA), as the name implies, is a particular variant of factor analysis (FA). There are different versions of FA and they serve several different purposes; some of them are presented in detail in this monograph. One immediate and important information revealed by FA is the rank of the matrix of data that are analyzed. This rank is an indication of the complexity of the process represented by the data. Often it can be understood as the number of components in the system, but as we will see later, this is not a rule; often it is better to see the rank as related to the number of processes followed by the measurements. In mathematical terms, the definition of rank is straightforward – it is the number of linearly independent rows/columns. In chemistry, there is a considerable variety of situations and no clear and general statements can be made; we then use the expression ‘chemical rank’.
- Relation
- Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Volume 2 p. 261-274
- Publisher Link
- http://dx.doi.org/10.1016/B978-044452701-1.00047-8
- Date
- 2009
- Publisher
- Elsevier
- Keyword(s)
-
Evolving factor analysis (EFA);
data analysis;
chemical rank
- Resource Type
- book chapter
- Identifier
- http://hdl.handle.net/1959.13/918388
- Identifier
- ISBN:9780444527042
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