Self-Weighted Adaptive Locality Discriminant Analysis

Muhan Guo, Feiping Nie, Xuelong Li

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

8 Scopus citations

Abstract

The linear discriminant analysis (LDA) is a popular technique for dimensionality reduction, nevertheless, when the input data lie in a complicated geometry distribution, LDA tends to obtain undesired results since it neglects the local structure of data. Though plenty of previous works devote to capturing the local structure, they have the same weakness that the neighbors found in the original data space may be not reliable, especially when noise is large. In this paper, we propose a novel supervised dimensionality reduction approach, Self-weighted Adaptive Locality Discriminant Analysis (SALDA), which aims to find a representative low-dimensional subspace of data. Compared with LDA and its variants, SALDA explores the neighborhood relationship of data points in the desired subspace effectively. Besides, the weights between within-class data points are learned automatically without setting any additional parameter. Extensive experiments on synthetic and real-world datasets show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages3378-3382
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
Country/TerritoryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • Linear discriminant analysis
  • Re-weighted method
  • Supervised dimensionality reduction

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