Tangent Phylogenetic PCA
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Dokumenter
- Tangent phylogenetic PCA
Indsendt manuskript, 1,15 MB, PDF-dokument
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.
Originalsprog | Engelsk |
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Titel | Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings |
Redaktører | Rikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen |
Forlag | Springer |
Publikationsdato | 2023 |
Sider | 77-90 |
ISBN (Trykt) | 9783031314377 |
DOI | |
Status | Udgivet - 2023 |
Begivenhed | 23nd Scandinavian Conference on Image Analysis, SCIA 2023 - Lapland, Finland Varighed: 18 apr. 2023 → 21 apr. 2023 |
Konference
Konference | 23nd Scandinavian Conference on Image Analysis, SCIA 2023 |
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Land | Finland |
By | Lapland |
Periode | 18/04/2023 → 21/04/2023 |
Navn | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Vol/bind | 13886 LNCS |
ISSN | 0302-9743 |
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.
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