Abstract

The Gifi system of analyzing categorical data through nonlinear varieties of classical multivariate analysis techniques is reviewed. The system is characterized by the optimal scaling of categorical variables which is implemented through alternating least squares algorithms. The main technique of homogeneity analysis is presented, along with its extensions and generalizations leading to nonmetric principal components analysis and canonical correlation analysis. Several examples are used to illustrate the methods. A brief account of stability issues and areas of applications of the techniques is also given.

Keywords

Categorical variableCanonical correlationMultivariate statisticsHomogeneity (statistics)Correspondence analysisPrincipal component analysisMathematicsMultivariate analysisStability (learning theory)ScalingNonlinear systemComputer scienceStatisticsData miningMachine learning

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Publication Info

Year
1998
Type
article
Volume
13
Issue
4
Citations
257
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George Michailidis, Jan de Leeuw (1998). The Gifi system of descriptive multivariate analysis. Statistical Science , 13 (4) . https://doi.org/10.1214/ss/1028905828

Identifiers

DOI
10.1214/ss/1028905828