A Novel Method to Solve the Separation Problem of LDA - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

A Novel Method to Solve the Separation Problem of LDA

(1) , (1) , (1)
1
Meng Zhang
  • Function : Author
  • PersonId : 1118816
Wei Li
  • Function : Author
  • PersonId : 1118817
Bo Zhang
  • Function : Author
  • PersonId : 1118818

Abstract

Linear discriminant analysis (LDA) is one of the most classical linear projection techniques for feature extraction, widely used in kinds of fields. Classical LDA is contributed to finding an optimal projection subspace that can maximize the between-class scatter and minimize the average within-class scatter of each class. However, the class separation problem always exists and classical LDA can not guarantee that the within-class scatter of each class get its minimum. In this paper, we proposed the k-classifiers method, which can reduce every within-class scatter of classes respectively and alleviate the class separation problem. This method will be applied in LDA and Norm LDA and achieve significant improvement. Extensive experiments performed on MNIST data sets demonstrate the effectiveness of k-classifiers.
Fichier principal
Vignette du fichier
498234_1_En_6_Chapter.pdf (234.25 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03456968 , version 1 (30-11-2021)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Meng Zhang, Wei Li, Bo Zhang. A Novel Method to Solve the Separation Problem of LDA. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.59-66, ⟨10.1007/978-3-030-46931-3_6⟩. ⟨hal-03456968⟩
11 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More