Dimensionality reduction in statistical learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the first key step in solving these tasks. The algorithms transform original high-dimensional data into lower dimensional representations in such a way that the initial task can be reduced to a lower dimensional one. The dimensionality reduction problems have varying formulations depending on their initial statistical learning tasks. A new geometrically motivated algorithm that solves various dimensionality reduction problems is presented.

Original languageEnglish
Title of host publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages330-335
Number of pages6
ISBN (Electronic)9781479974153
DOIs
Publication statusPublished - 5 Feb 2014
Externally publishedYes
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: 3 Dec 20146 Dec 2014

Publication series

NameProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Country/TerritoryUnited States
CityDetroit
Period3/12/146/12/14

Keywords

  • dimensionality reduction
  • manifold learning
  • statistical learning
  • tangent bundle manifold learning
  • unsupervised learning

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