<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Plaku, E.</style></author><author><style face="normal" font="default" size="100%">L. E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Dimensionality Reduction Using Approximate Nearest Neighbors</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM International Conference on Data Mining (SDM)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword><keyword><style  face="normal" font="default" size="100%">project_Proximity</style></keyword><keyword><style  face="normal" font="default" size="100%">proximity relations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.siam.org/proceedings/datamining/2007/dm07_preface.php</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Minneapolis, Minnesota</style></pub-location><pages><style face="normal" font="default" size="100%">3711-3716</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Nonlinear dimensionality reduction methods often rely on the
               nearest-neighbors graph to extract low-dimensional embeddings that
               reliably capture the underlying structure of high-dimensional
               data. Research however has shown that computing nearest neighbors of a
               point from a high-dimensional data set generally requires time
               proportional to the size of the data set itself, rendering the
               computation of the nearest-neighbors graph prohibitively expensive.

               This work significantly reduces the major computational bottleneck of
               many nonlinear dimensionality reduction methods by efficiently and
               accurately approximating the nearest-neighbors graph. The
               approximation relies on a distance-based projection of
               high-dimensional data onto low-dimensional Euclidean spaces. As
               indicated by experimental results, the advantage of the proposed
               approximation is that while it reliably maintains the accuracy of
               nonlinear dimensionality reduction methods, it significantly reduces
               the computational time.</style></abstract><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record></records></xml>
