Tangent Phylogenetic PCA

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt


Phylogenetic PCA (p-PCA) is a version of PCA for observations that are leaf nodes of a phylogenetic tree. P-PCA accounts for the fact that such observations are not independent, due to shared evolutionary history. The method works on Euclidean data, but in evolutionary biology there is a need for applying it to data on manifolds, particularly shapes. We provide a generalization of p-PCA to data lying on Riemannian manifolds, called Tangent p-PCA. Tangent p-PCA thus makes it possible to perform dimension reduction on a data set of shapes, taking into account both the non-linear structure of the shape space as well as phylogenetic covariance. We show simulation results on the sphere, demonstrating well-behaved error distributions and fast convergence of estimators. Furthermore, we apply the method to a data set of mammal jaws, represented as points on a landmark manifold equipped with the LDDMM metric.

TitelImage Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
RedaktørerRikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
ISBN (Trykt)9783031314377
StatusUdgivet - 2023
Begivenhed23nd Scandinavian Conference on Image Analysis, SCIA 2023 - Lapland, Finland
Varighed: 18 apr. 202321 apr. 2023


Konference23nd Scandinavian Conference on Image Analysis, SCIA 2023
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind13886 LNCS

Bibliografisk note

Funding Information:
M.A. and X.P. are supported by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (grantagree-ment G-Statistics No. 786854). S.S. is partly supported by Novo Nordisk Foundation grant NNF18OC0052000 as well as VILLUM FONDEN research grant 40582 and UCPH Data+ Strategy 2023 funds for interdisciplinary research.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk

Ingen data tilgængelig

ID: 357280553