 Scale and curvature effects in principal geodesic analysisDrew Lazar and Lizhen Lin Journal of Multivariate Analysis, 2017, vol. 153, issue C, 64-82 Abstract: There is growing interest in using the close connection between differential geometry and statistics to model smooth manifold-valued data. In particular, much work has been done recently to generalize principal component analysis (PCA), the method of dimension reduction in linear spaces, to Riemannian manifolds. One such generalization is known as principal geodesic analysis (PGA). This paper, in a novel fashion, obtains Taylor expansions in scaling parameters introduced in the domain of objective functions in PGA. It is shown this technique not only leads to better closed-form approximations of PGA but also reveals the effects that scale, curvature and the distribution of data have on solutions to PGA and on their differences to first-order tangent space approximations. This approach should be able to be applied not only to PGA but also to other generalizations of PCA and more generally to other intrinsic statistics on Riemannian manifolds. Keywords: Curvature effects; Data scaling; Diffusion tensors; Dimension reduction; Manifold-valued statistics; Principal geodesic analysis (PGA); Symmetric spaces (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:153:y:2017:i:c:p:64-82 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2016.09.009 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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