Theory and applications of b-bit minwise hashing

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Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “Utilizing minwise hashing [2, 15], which is the LSH for resemblance and is known to outperform SRP in sparse data [23], we can construct an even better ALSH (i.”
Recent Advances of Large-Scale Linear Classification (2012, background): “Examples include random Fourier projection [77], random projections [78], [79], polynomial approximation [80], and hashing [81]– [84].”
Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, methodology): “Minwise hashing (or minhash) [6, 7, 27] is a popular technique deployed by big data industries for these tasks.”
Consistent Weighted Sampling Made Fast, Small, and Easy (2014, methodology): “For a scheme such as the one we use [15], L being a small constant such as 5 suffices.”
Discrete Graph Hashing (2014, methodology): “The well-known representatives include Min-wise Hashing (MinHash) [3] and Locality-Sensitive Hashing (LSH) [2].”