Nearly optimal linear embeddings into very low dimensions

Nearly optimal linear embeddings into very low dimensions

We propose algorithms for constructing linear embeddings of a finite dataset V ⊂ ℝ[superscript d] into a k-dimensional subspace with provable, nearly optimal distortions. First, we propose an exhaustive-search-based algorithm that yields a k-dimensional linear embedding with distortion at most ε[sub...

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Bibliographic Details
Main Authors: Grant, Elyot (Contributor), Hegde, Chinmay (Contributor), Indyk, Piotr (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2018-02-14T20:36:21Z.
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