Timezone: »
Poster
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Anshumali Shrivastava · Ping Li
We present the first provably sublinear time hashing algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Searching with (un-normalized) inner product as the underlying similarity measure is a known difficult problem and finding hashing schemes for MIPS was considered hard. While the existing Locality Sensitive Hashing (LSH) framework is insufficient for solving MIPS, in this paper we extend the LSH framework to allow asymmetric hashing schemes. Our proposal is based on a key observation that the problem of finding maximum inner products, after independent asymmetric transformations, can be converted into the problem of approximate near neighbor search in classical settings. This key observation makes efficient sublinear hashing scheme for MIPS possible. Under the extended asymmetric LSH (ALSH) framework, this paper provides an example of explicit construction of provably fast hashing scheme for MIPS. Our proposed algorithm is simple and easy to implement. The proposed hashing scheme leads to significant computational savings over the two popular conventional LSH schemes: (i) Sign Random Projection (SRP) and (ii) hashing based on $p$-stable distributions for $L_2$ norm (L2LSH), in the collaborative filtering task of item recommendations on Netflix and Movielens (10M) datasets.
Author Information
Anshumali Shrivastava (Rice University / ThirdAI Corp.)
Ping Li (Baidu Research USA)
Related Events (a corresponding poster, oral, or spotlight)
-
2014 Oral: Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) »
Tue. Dec 9th 04:20 -- 04:40 PM Room Level 2, room 210
More from the Same Authors
-
2017 Poster: Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery »
Jie Shen · Ping Li -
2017 Poster: Simple strategies for recovering inner products from coarsely quantized random projections »
Ping Li · Martin Slawski -
2016 Poster: Exact Recovery of Hard Thresholding Pursuit »
Xiaotong Yuan · Ping Li · Tong Zhang -
2016 Poster: Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation »
Xiaotong Yuan · Ping Li · Tong Zhang · Qingshan Liu · Guangcan Liu -
2016 Poster: Quantized Random Projections and Non-Linear Estimation of Cosine Similarity »
Ping Li · Michael Mitzenmacher · Martin Slawski -
2015 Poster: b-bit Marginal Regression »
Martin Slawski · Ping Li -
2015 Spotlight: b-bit Marginal Regression »
Martin Slawski · Ping Li -
2015 Poster: Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices »
Martin Slawski · Ping Li · Matthias Hein -
2014 Poster: Recovery of Coherent Data via Low-Rank Dictionary Pursuit »
Guangcan Liu · Ping Li -
2014 Poster: Online Optimization for Max-Norm Regularization »
Jie Shen · Huan Xu · Ping Li -
2014 Spotlight: Recovery of Coherent Data via Low-Rank Dictionary Pursuit »
Guangcan Liu · Ping Li -
2013 Poster: Beyond Pairwise: Provably Fast Algorithms for Approximate $k$-Way Similarity Search »
Anshumali Shrivastava · Ping Li -
2013 Poster: Sign Cauchy Projections and Chi-Square Kernel »
Ping Li · Gennady Samorodnitsk · John Hopcroft -
2012 Poster: Entropy Estimations Using Correlated Symmetric Stable Random Projections »
Ping Li · Cun-Hui Zhang -
2012 Poster: One Permutation Hashing »
Ping Li · Art B Owen · Cun-Hui Zhang -
2011 Poster: Hashing Algorithms for Large-Scale Learning »
Ping Li · Anshumali Shrivastava · Joshua L Moore · Arnd C König -
2010 Spotlight: b-Bit Minwise Hashing for Estimating Three-Way Similarities »
Ping Li · Arnd C König · Wenhao Gui -
2010 Poster: b-Bit Minwise Hashing for Estimating Three-Way Similarities »
Ping Li · Arnd C König · Wenhao Gui -
2008 Poster: One sketch for all: Theory and Application of Conditional Random Sampling »
Ping Li · Kenneth W Church · Trevor Hastie -
2008 Spotlight: One sketch for all: Theory and Application of Conditional Random Sampling »
Ping Li · Kenneth W Church · Trevor Hastie -
2007 Spotlight: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting »
Ping Li · Chris J Burges · Qiang Wu -
2007 Poster: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting »
Ping Li · Chris J Burges · Qiang Wu -
2007 Poster: A Unified Near-Optimal Estimator For Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Sta »
Ping Li · Trevor Hastie -
2006 Poster: Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data »
Ping Li · Kenneth W Church · Trevor Hastie