F
Frank Nielsen
Researcher at Association for Computing Machinery
Publications - 393
Citations - 9047
Frank Nielsen is an academic researcher from Association for Computing Machinery. The author has contributed to research in topics: Cluster analysis & Exponential family. The author has an hindex of 45, co-authored 373 publications receiving 7811 citations. Previous affiliations of Frank Nielsen include Aarhus University & DONG Energy.
Papers
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Statistical region merging
Richard Nock,Frank Nielsen +1 more
TL;DR: A statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions is explored, leading to a fast segmentation algorithm tailored to processing images described using most common numerical pixel attribute spaces.
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On weighting clustering
Richard Nock,Frank Nielsen +1 more
TL;DR: This paper handles clustering as a constrained minimization of a Bregman divergence, and theoretical results show benefits resembling those of boosting algorithms and bring modified (weighted) versions of clustering algorithms such as k-means, fuzzy c-mean, expectation maximization (EM), and k-harmonic means.
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Sided and Symmetrized Bregman Centroids
Frank Nielsen,Richard Nock +1 more
TL;DR: It is proved that all three centroids are unique and give closed-form solutions for the sided centroid that are generalized means, and a provably fast and efficient arbitrary close approximation algorithm for the symmetrized centroid based on its exact geometric characterization is designed.
Proceedings Article
DeepBach: a steerable model for bach chorales generation
TL;DR: DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces, is introduced, which is capable of generating highly convincing chorales in the style of Bach.
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The Burbea-Rao and Bhattacharyya Centroids
Frank Nielsen,Sylvain Boltz +1 more
TL;DR: An efficient algorithm for computing the Bhattacharyya centroid of a set of parametric distributions belonging to the same exponential families, improving over former specialized methods found in the literature that were limited to univariate or “diagonal” multivariate Gaussians.