Objective Similarity search is the task of identifying, in a collection of items, the ones that are “similar” to a givenquery item. This task has a range of important applications (e.g. in information retrieval, patternrecognition, statistics, and machine learning) where data sets are often big, high dimensional, andpossibly noisy. State-of-the-art methods for similarity search offer only weak guarantees when faced withbig data. Either the space overhead is excessive (1000s of times larger than the space for the data itself),or the work needed to report the similar items may be comparable to the work needed to go through allitems (even if just a tiny fraction of the items are similar). As a result, many applications have to resort tothe use of ad-hoc solutions with only weak theoretical guarantees.This proposal aims at strengthening the theoretical foundation of scalable similarity search, anddeveloping novel practical similarity search methods backed by theory. In particular we will:- Leverage new types of embeddings that are kernelized, asymmetric, and complex-valued.- Consider statistical models of noise in data, and design similarity search data structures whoseperformance guarantees are phrased in statistical terms.- Build a new theory of the communication complexity of distributed, dynamic similarity search,emphasizing the communication bottleneck present in modern computing infrastructures.The objective is to produce new methods for similarity search that are: 1) Provably robust, 2) scalableto large and high-dimensional data sets, 3) substantially more resource efficient than current state-ofthe-art solutions, and 4) able to provide statistical guarantees on query answers.The study of similarity search has been an incubator for techniques (e.g. locality-sensitive hashing andrandom projections) that have wide-ranging applications. The new techniques developed in this projectare likely to have significant impacts beyond similarity search. Fields of science natural sciencescomputer and information sciencesdata sciencebig datanatural sciencescomputer and information sciencesartificial intelligencepattern recognitionnatural sciencesmathematicsapplied mathematicsstatistics and probabilitynatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) FP7-IDEAS-ERC - Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Topic(s) ERC-CG-2013-PE6 - ERC Consolidator Grant - Computer Science and Informatics Call for proposal ERC-2013-CoG See other projects for this call Funding Scheme ERC-CG - ERC Consolidator Grants Host institution IT-UNIVERSITETET I KOBENHAVN EU contribution € 1 889 711,73 Address RUED LANGGAARDSVEJ 7 2300 Kobenhavn Denmark See on map Region Danmark Hovedstaden Byen København Activity type Higher or Secondary Education Establishments Administrative Contact Georg Dam Steffensen (Mr.) Principal investigator Rasmus Pagh (Dr.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data Beneficiaries (1) Sort alphabetically Sort by EU Contribution Expand all Collapse all IT-UNIVERSITETET I KOBENHAVN Denmark EU contribution € 1 889 711,73 Address RUED LANGGAARDSVEJ 7 2300 Kobenhavn See on map Region Danmark Hovedstaden Byen København Activity type Higher or Secondary Education Establishments Administrative Contact Georg Dam Steffensen (Mr.) Principal investigator Rasmus Pagh (Dr.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data