Learning discriminant isomap for dimensionality reduction

Bo Yang, Ming Xiang, Yupei Zhang

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

3 Scopus citations

Abstract

In order to extend the unsupervised nonlinear dimensionality reduction method Isomap for use in supervised learning, a new supervised manifold learning method namely discriminant Isomap (D-Isomap) is proposed, in which the geometrical structure of each class data is preserved by keeping geodesic distances between data points of the same class and the discriminant capacity is enhanced by maximizing the distances between data points of different classes. A new objective function is defined for this purpose and the corresponding optimization problem is solved by using the SMACOF algorithm. The effectiveness of D-Isomap is examined by extensive simulations on artificial and real-world data sets, including MNIST, USPS, and UCI. In both visualization and classification experiments, D-Isomap achieves comparable or better performance than the widely used dimensionality reduction algorithms.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
StatePublished - 28 Sep 2015
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2015-September

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2015
Country/TerritoryIreland
CityKillarney
Period12/07/1517/07/15

Keywords

  • Electronics packaging
  • Visualization

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