Geometric data analysis
From Wikipedia, the free encyclopedia
Geometric data analysis can refer to geometric aspects of image analysis, pattern analysis and shape analysis or the approach of multivariate statistics that treats arbitrary data sets as clouds of points in n-dimensional space. This includes topological data analysis, cluster analysis, inductive data analysis, correspondence analysis, multiple correspondence analysis and principal components analysis.
See also
- Combinatorial data analysis
- Structured data analysis (statistics)
- Algebraic statistics for algebraic-geometry in statistics
References
- 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
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