Geometric data analysis
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.
See also
[edit]- 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
[edit]- 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
[edit]- 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