PDF-NearOptimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions by

In the first part we survey a family of nearest neighbor algorithms that are based on the concept of locality sensitive hashing Many of these algorithm have already been successfully applied in a variety of practical scenarios In the second part of. edu Piotr Indyk MIT indykmitedu Abstract We present an algorithm for the approximate near est neighbor problem in a dimensional Euclidean space achieving query time of dn c 1 and space dn 11 c 1 This almost matches the lower bound for hashingbased a Such matrices has several attractive properties they support algorithms with low computational complexity and make it easy to perform in cremental updates to signals We discuss applications to several areas including compressive sensing data stream A. pproximate . N. ear . N. eighbors. Alexandr Andoni . (Simons Inst. . /. . Columbia). Ilya Razenshteyn . (MIT, now at IBM . Almaden. ). Near Neighbor Search. Dataset: . points in . , . Goal: . a data point within . LECTURE 10. Classification. . k-nearest neighbor classifier. . Naïve Bayes. . Logistic Regression. . Support Vector Machines. NEAREST NEIGHBOR CLASSIFICATION. Instance-Based Classifiers. Store the training records . COL 106. Shweta Agrawal, . Amit. Kumar. Slide Courtesy : Linda Shapiro, . Uwash. Douglas W. Harder, . UWaterloo. 12/26/03. Hashing - Lecture 10. 2. The Need for Speed. Data structures we have looked at so far. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . Jie Bao Chi-Yin Chow Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota – Twin Cities. Wei-Shinn Ku. Department of Computer Science and Software Engineering. Josef . Sivic. http://. www.di.ens.fr. /~josef. INRIA, . WILLOW, ENS/INRIA/CNRS UMR 8548. Laboratoire. . d’Informatique. , . Ecole. . Normale. . Supérieure. , Paris. With slides from: O. Chum, K. . Similarity Search. Alex . Andoni. (Columbia University). Nearest Neighbor Search (NNS). Preprocess: . a set . . of points. Query:. given a . query point . , report a point . with the smallest distance to . Naifan Zhuang, Jun Ye, Kien A. Hua. Department of Computer Science. University of Central Florida. ICPR 2016. Presented by Naifan Zhuang. Motivation and Background. According to a report from Cisco, by 2019:. L. ocality-. S. ensitive . H. ashing. Alexandr . Andoni. . (Columbia). Ilya Razenshteyn . (MIT CSAIL). Near Neighbor Search. Dataset: . points in . , . Goal: . a data point within . from a query. Space, query time. Ilya Razenshteyn . (MIT CSAIL). j. oint with. Alexandr Andoni . (Columbia University). Aleksandar Nikolov . (University of Toronto). Erik Waingarten . (Columbia University). arXiv:1611.06222. Motivation. Exact Nearest Neighbor Algorithms Sabermetrics One of the best players ever .310 batting average 3,465 hits 260 home runs 1,311 RBIs 14x All-star 5x World Series winner Who is the next Derek Jeter? Derek Jeter ℓ. p. –spaces (2<p<∞) via . embeddings. Yair. . Bartal. . Lee-Ad Gottlieb Hebrew U. Ariel University. Nearest neighbor search. Problem definition:. Given a set of points S, preprocess S so that the following query can be answered efficiently:.