Geometric data analysis comprises geometric aspects of image analysis, pattern analysis, and shape analysis, and the approach of multivariate statistics, which treat arbitrary data sets as clouds of points in a space that is n-dimensional. This includes topological data analysis, cluster analysis, inductive data analysis, correspondence analysis, multiple correspondence analysis, principal components analysis and iconography of correlations.
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See also
- Algebraic statistics for algebraic-geometry in statistics
- Combinatorial data analysis
- Computational anatomy for the study of shapes and forms at the morphome scale
- Structured data analysis (statistics)
References
- Michael Kirby (2001). Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns. Wiley. ISBN 978-0-4712-3929-1.
- Brigitte Le Roux, Henry Rouanet (2004). Geometric Data Analysis: from Correspondence Analysis to Structured Data Analysis. Springer. ISBN 978-1-4020-2235-7.
- Michael J. Greenacre, Jörg Blasius (2006). Multiple Correspondence Analysis and Related Methods. CRC press. ISBN 978-1-58488-628-0.
- Approximation of Geodesic Distances for Geometric Data Analysis
Differential geometry and data analysis
- Differential Geometry and Statistics, M.K. Murray, J.W. Rice, Chapman and Hall/CRC, ISBN 978-0-412-39860-5
- Ridges in image and data analysis, David Eberly, Springer, 1996, ISBN 978-0-7923-4268-7
This page was last edited on 11 January 2024, at 21:48