Math @ Duke

Publications [#328839] of HauTieng Wu
Papers Published
 Singer, A; Wu, HT, Vector Diffusion Maps and the Connection Laplacian.,
Communications on Pure & Applied Mathematics, vol. 65 no. 8
(August, 2012) [doi]
(last updated on 2018/07/16)
Abstract: We introduce vector diffusion maps (VDM), a new mathematical framework for organizing and analyzing massive highdimensional data sets, images, and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other nonlinear dimensionality reduction methods, such as LLE, ISOMAP, and Laplacian eigenmaps. While existing methods are either directly or indirectly related to the heat kernel for functions over the data, VDM is based on the heat kernel for vector fields. VDM provides tools for organizing complex data sets, embedding them in a lowdimensional space, and interpolating and regressing vector fields over the data. In particular, it equips the data with a metric, which we refer to as the vector diffusion distance. In the manifold learning setup, where the data set is distributed on a lowdimensional manifold ℳ (d) embedded in ℝ (p) , we prove the relation between VDM and the connection Laplacian operator for vector fields over the manifold.


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