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• W2123948358 abstract "Neighbor embedding (NE) methods have found their use in visualization but are limited in analysis tasks due to their O(n2) complexity for n samples. We demonstrate that the obvious approach of subsampling produces inferior results and propose a generic approximated optimization technique that reduces the NE optimization cost to O(n log n). The technique is based on realizing that in visualization the embedding space is necessarily very low-dimensional (2D or 3D), and hence efficient approximations developed for n-body force calculations can be applied. In gradient-based NE algorithms the gradient for an individual point decomposes into forces exerted by the other points. The contributions of close-by points need to be computed individually but far-away points can be approximated by their center of mass, rapidly computable by applying a recursive decomposition of the visualization space into quadrants. The new algorithm brings a significant speed-up for medium-size data, and brings big data within reach of visualization." @default.
• W2123948358 created "2016-06-24" @default.
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• W2123948358 date "2013-06-16" @default.
• W2123948358 modified "2023-09-24" @default.
• W2123948358 title "Scalable Optimization of Neighbor Embedding for Visualization" @default.
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