Читать книгу Multiblock Data Fusion in Statistics and Machine Learning - Tormod Næs - Страница 39

The history of multiblock data analysis methods goes back a long time. One of the starting points was principal component analysis (PCA, Pearson (1901)). Another early method in statistics was canonical correlation analysis (Hotelling, 1936a) which led to development of many related methods. In the social sciences, path-models were developed, such as LISREL and PLS (Jöreskog and Wold, 1982) which are also used in consumer science and marketing research. In psychometrics, inter-battery factor analysis (Tucker, 1958) was developed and later simultaneous component analysis methods (Ten Berge et al., 1992) which have also sparked many alternatives. In parallel in chemometrics, methods such as consensus PCA and hierarchical PCA were developed (Westerhuis et al., 1998). Ideas on the latter two methods already started in the 1970s at conferences. In the French data analysis school, multiple correspondence analysis was developed (Benzécri, 1980) and many other methods. In multiway data analysis (which can be regarded as a subset of multiblock data analysis methods) the earliest developments started with the work of Cattell (1944), Carroll and Chang (1970) and Harshman (1970). For a history of the latter, see Smilde et al. (2004). Figure 1.8 tries to systematically show many multiblock data analysis methods and how they relate to basic statistical methodology and subspace projection methods from various fields of applied data analysis.

Figure 1.8 Phylogeny of some multiblock methods and relations to basic data analysis methods used in this book.