2024-03-29T02:11:29Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1698732023-09-20T11:10:11Zcom_10234_201964com_10234_9com_10234_7037col_10234_201449col_10234_8635
Repositori UJI
author
Epifanio, Irene
author
Ibáñez Gual, Maria Victoria
author
Simó, Amelia
2017-11-06T08:35:02Z
2017-11-06T08:35:02Z
2017-10
EPIFANIO, Irene; IBÁÑEZ, María Victoria; SIMÓ, Amelia. Archetypal shapes based on landmarks and extension to handle missing data. Advances in Data Analysis and Classification, 2017, p. 1-31.
http://hdl.handle.net/10234/169873
http://dx.doi.org/:10.1007/s11634-017-0297-7
Archetype and archetypoid analysis are extended to shapes. The objective is to find representative shapes. Archetypal shapes are pure (extreme) shapes. We focus on the case where the shape of an object is represented by a configuration matrix of landmarks. As shape space is not a vectorial space, we work in the tangent space, the linearized space about the mean shape. Then, each observation is approximated by a convex combination of actual observations (archetypoids) or archetypes, which are a convex combination of observations in the data set. These tools can contribute to the understanding of shapes, as in the usual multivariate case, since they lie somewhere between clustering and matrix factorization methods. A new simplex visualization tool is also proposed to provide a picture of the archetypal analysis results. We also propose new algorithms for performing archetypal analysis with missing data and its extension to incomplete shapes. A well-known data set is used to illustrate the methodologies developed. The proposed methodology is applied to an apparel design problem in children.
eng
© 2017 Springer International Publishing AG. Part of Springer Nature.
statistical shape analysis
archetype analysis
archetypoid analysis
anthropometric data
children’s wear
missing data
Archetypal shapes based on landmarks and extension to handle missing data
info:eu-repo/semantics/article
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