<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benyamin Ghojogh</style></author><author><style face="normal" font="default" size="100%">Mark Crowley</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Instance Ranking and Numerosity Reduction Using Matrix Decompositionand Subspace Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Canadian Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer’s Lecture Notes in Artificial Intelligence.</style></publisher><pub-location><style face="normal" font="default" size="100%">Kingston, ON, Canada</style></pub-location><pages><style face="normal" font="default" size="100%">12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	One way to deal with the ever increasing amount of available data for processing is to rank data instances by usefulness and reduce the dataset size. In this work, we introduce a framework to achieve this using matrix decomposition and subspace learning. Our central contribution is a novel similarity measure for data instances that uses the basis obtained from matrix decomposition of the dataset. Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction. We then validate the effectiveness of these algorithms for data reduction on several datasets for classification, regression, and clustering tasks.
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