Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “[20] used a related (asymmetric) idea for solving 3-way similarity search.”
One Permutation Hashing (2012, background): “In machine learning practice, the use of extremely high-dimensional data has become common.”
Asymmetric Minwise Hashing (2014, background): “Another potentially promising topic for future work might be asymmetric minwise hashing for 3-way (or higher-order) similarities [25, 34]”
Improved Densification of One Permutation Hashing (2014, background): “…hashing is a popular indexing technique for a variety of large-scale data processing applications, which include duplicate detection [4, 13], all-pair similarity [3], fast linear learning [19], temporal correlation [10], 3-way similarity & retrieval [17, 23], graph algorithms [6, 11, 21], and more.”
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search (2014, methodology): “CRS naturally extends to multi-way similarity (Li et al., 2010; Shrivastava & Li, 2013) and the method is not restricted to binary data.”
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS) (2014, methodology): “Prior to our work, asymmetry was applied for hashing higher order similarity [22], sketching [8], hashing different subspaces [3], a nd data dependent hashing [20] which unlike locality sensitive hashing do not come with provable runtime guarantees.”