Improved Bounds on the Dot Product under Random Projection and Random Sign Projection

@inproceedings{Kabn2015ImprovedBO,
  title={Improved Bounds on the Dot Product under Random Projection and Random Sign Projection},
  author={Ata Kab{\'a}n},
  booktitle={KDD},
  year={2015}
}
Dot product is a key building block in a number of data mining algorithms from classification, regression, correlation clustering, to information retrieval and many others. When data is high dimensional, the use of random projections may serve as a universal dimensionality reduction method that provides both low distortion guarantees and computational savings. Yet, contrary to the optimal guarantees that are known on the preservation of the Euclidean distance cf. the Johnson-Lindenstrauss lemma… CONTINUE READING

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Extensions of Lipschitz mappings into a Hilbert space. Conference in Modern Analysis and Probability (New Haven, Conn

W.B.Johnson, J. Lindenstrauss
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