NSK FJLT-3528 bearing high technology in India
machine_learning - GitHub
Ailon, Nir, and Bernard Chazelle. "The fast Johnson-Lindenstrauss transform and approximate nearest neighbors." SIAM Journal on Computing 39.1 (2009): 302-322. Available: https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf .Applications of Machine Learning to Location Data - Using machine learning to
Almost Optimal Explicit Johnson-Lindenstrauss Families - Electronic
Lindenstrauss Transform (FJLT) of [2]. For clarity, we concentrate on the case of δ = Θ(1/dc) polynomially small. The case of general δ can be handled simi- larly with some minor technical issues3 that we skip in this extended abstract. Further, we assume that log(1/δ)/ε2 < d as else JLL is not interesting. As outlined in the
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