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dc.contributor.advisor Daubechies, Ingrid
dc.contributor.author Gao, Tingran
dc.date.accessioned 2015-05-12T20:46:21Z
dc.date.available 2015-05-12T20:46:21Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/10161/9931
dc.description Dissertation
dc.description.abstract <p>We introduce Hypoelliptic Diffusion Maps (HDM), a novel semi-supervised machine learning framework for the analysis of collections of anatomical surfaces. Triangular meshes obtained from discretizing these surfaces are high-dimensional, noisy, and unorganized, which makes it difficult to consistently extract robust geometric features for the whole collection. Traditionally, biologists put equal numbers of ``landmarks'' on each mesh, and study the ``shape space'' with this fixed number of landmarks to understand patterns of shape variation in the collection of surfaces; we propose here a correspondence-based, landmark-free approach that automates this process while maintaining morphological interpretability. Our methodology avoids explicit feature extraction and is thus related to the kernel methods, but the equivalent notion of ``kernel function'' takes value in pairwise correspondences between triangular meshes in the collection. Under the assumption that the data set is sampled from a fibre bundle, we show that the new graph Laplacian defined in the HDM framework is the discrete counterpart of a class of hypoelliptic partial differential operators.</p><p>This thesis is organized as follows: Chapter 1 is the introduction; Chapter 2 describes the correspondences between anatomical surfaces used in this research; Chapter 3 and 4 discuss the HDM framework in detail; Chapter 5 illustrates some interesting applications of this framework in geometric morphometrics.</p>
dc.subject Mathematics
dc.subject Diffusion Maps
dc.subject Fibre Bundles
dc.subject Graph Laplacian
dc.subject Hypoellipticity
dc.subject Machine Learning
dc.subject Riemannian Geometry
dc.title Hypoelliptic Diffusion Maps and Their Applications in Automated Geometric Morphometrics
dc.type Dissertation
dc.department Mathematics


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