We develop a method for tracing out the shape of a cloud of sample observations, in arbitrary dimensions, called the data cloud wrapper (DCW). The DCW have strong theoretical properties, have algorithmic scalability and parallel computational features. We further use the DCW to develop a new fast, robust and accurate classification method in high dimensions, called the geometric learning algorithm (GLA). Two of the main features of the proposed algorithm are that there are no assumptions made about the geometric properties of the underlying data generating distribution, and that there are no parametric or other restrictive assumptions made either for the data or the algorithm. The proposed methods are typically faster and more robust than established classification techniques, while being comparably accurate in most cases.
|Original language||English (US)|
|Title of host publication||Advances in Data Mining|
|Subtitle of host publication||Applications and Theoretical Aspects - 15th Industrial Conference, ICDM 2015, Proceedings|
|Number of pages||15|
|State||Published - 2015|
|Event||15th Industrial Conference on Data Mining, ICDM 2015 - Hamburg, Germany|
Duration: Jul 11 2015 → Jul 24 2015
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th Industrial Conference on Data Mining, ICDM 2015|
|Period||7/11/15 → 7/24/15|
Bibliographical noteFunding Information:
This research is partially supported by NSF grant # IIS-1029711, NASA grant #-1502546) the Institute on the Environment (IonE), and College of Liberal Arts (CLA) at the University of Minnesota.
© Springer International Publishing Switzerland 2015.