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To this goal, we present a new class of locally semi-parametric estimators that allows analysis of accuracy with finite samples, as well as explicitly addresses the problem of selecting optimal support volume for local fitting. Experiments on synthetic and real data validate the behavior predicted by the model, and show competitive performance and improved stability over leading alternatives that require a preset scale.<\/jats:p>","DOI":"10.1142\/s0218195910003438","type":"journal-article","created":{"date-parts":[[2010,11,1]],"date-time":"2010-11-01T09:26:48Z","timestamp":1288603608000},"page":"543-575","source":"Crossref","is-referenced-by-count":10,"title":["SCALE SELECTION FOR GEOMETRIC FITTING IN NOISY POINT CLOUDS"],"prefix":"10.1142","volume":"20","author":[{"given":"RANJITH","family":"UNNIKRISHNAN","sequence":"first","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JEAN-FRAN\u00c7OIS","family":"LALONDE","sequence":"additional","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"NICOLAS","family":"VANDAPEL","sequence":"additional","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MARTIAL","family":"HEBERT","sequence":"additional","affiliation":[{"name":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1007\/PL00009475"},{"key":"rf8","volume-title":"Spectral Graph Theory","author":"Chung F. 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