PPT-Theory of Locality Sensitive Hashing

CS246 Mining Massive Datasets Jure Leskovec Stanford University httpcs246stanfordedu Recap Finding similar documents Task Given a large number N in the millions or billions of documents find near duplicates. of Electrical Engineering Tel Aviv University simonkormailtauacil avidanengtauacil Abstract Coherency Sensitive Hashing CSH extends Locality Sensitivity Hashing LSH and PatchMatch to quickly 64257nd matching patches between two images LSH relies on uiucedu Xiaofei He Yahoo Research Labs hexyahooinccom Kun Zhou Microsoft Research Asia kunzhoumicrosoftcom Jiawei Han Department of Computer Science University of Illinois at Urbana Champaign hanjcsuiucedu Hujun Bao College of Computer Science Zhejia The analysis uses only very basic and intuitively understandable concepts of probability theory and is meant to be accessible even for undergraduates taking their 64257rst algorithms course 1 Introduction dictionary is a data structure for storing a Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th Does Bell’s theorem prevent the use of causal explanations in quantum mechanics?. Part I:. Locality, Bell’s version of locality, and its discontents. The greatest mystery in science?. Locality. = “things do not go faster than . Yunchao. Gong. UNC Chapel Hill. yunchao@cs.unc.edu. The problem. Large scale image search:. We have a candidate image. Want to search a . large database . to find similar images. Search the . internet. Lecture 6: Locality Sensitive Hashing (LSH). Nearest Neighbor . Given a set P of n points in R. d. Nearest Neighbor . Want to build a data structure to answer nearest neighbor queries. Voronoi. Diagram. Madhu. . Sudan. Harvard. April 9, 2016. Skoltech: Locality in Coding Theory. 1. Error-Correcting Codes. (Linear) Code . .. : Finite field with . elements.. . block length. . message length. : Rate of . Madhu Sudan. Harvard. April 9, 2016. Skoltech: Locality in Coding Theory. 1. Outline of this Part. Three ideas in Testing:. Tensor Products. Composition/Recursion. Symmetry (esp. affine-invariance. ). Plan. I spent the last decade advising on numerous cases where hash tables/functions were used. A few observations on . What data structures I’ve seen implemented and where. What do developers think, were they need help. In static hashing, function . h. maps search-key values to a fixed set of . B. . buckets, that contain a number of (K,V) entries.. . . Problem: d. atabases . grow . (or shrink) . with time. . If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows.. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. 3. William Cohen. 1. Outline. Randomized methods - so far. SGD with the hash trick. Bloom filters. count-min sketches. Today:. Review and discussion. More on count-min. Morris counters. locality sensitive hashing. Amjad. . Daoud. , Ph.D.. http://iswsa.acm.org/mphf. Practical Perfect Hashing for very large Key-Value Databases . Abstract. This presentation describes a practical algorithm for perfect hashing that is suitable for very large KV (key, value)...