IEEE Trans Pattern Anal Mach Intell - Hashing Hyperplane Queries to Near Points with Applications to Large-scale Active Learning.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
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{ first(2504) two(1366) second(1323) }
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{ care(1570) inform(1187) nurs(1089) }
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{ case(1353) use(1143) diagnosi(1136) }
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{ model(2656) set(1616) predict(1553) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We consider the problem of retrieving the database points nearest to a given hyperplane query without exhaustively scanning the entire database. For this problem, we propose two hashing-based solutions. Our first approach maps the data to two-bit binary keys that are locality-sensitive for the angle between the hyperplane normal and a database point. Our second approach embeds the data into a vector space where the Euclidean norm reflects the desired distance between the original points and hyperplane query. Both use hashing to retrieve near points in sub-linear time. Our first method's preprocessing stage is more efficient, while the second has stronger accuracy guarantees. We apply both to pool-based active learning: taking the current hyperplane classifier as a query, our algorithm identifies those points (approximately) satisfying the well-known minimal distance-to-hyperplane selection criterion. We empirically demonstrate our methods' tradeoffs, and show that they make it practical to perform active selection with millions of unlabeled points.

Resumo Limpo

consid problem retriev databas point nearest given hyperplan queri without exhaust scan entir databas problem propos two hashingbas solut first approach map data twobit binari key localitysensit angl hyperplan normal databas point second approach emb data vector space euclidean norm reflect desir distanc origin point hyperplan queri use hash retriev near point sublinear time first method preprocess stage effici second stronger accuraci guarante appli poolbas activ learn take current hyperplan classifi queri algorithm identifi point approxim satisfi wellknown minim distancetohyperplan select criterion empir demonstr method tradeoff show make practic perform activ select million unlabel point

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