Using Double Regularization to Improve the Effectiveness and Robustness of Fisher Discriminant Analysis as A Projection Technique

Yuechi Jiang, H. F. Frank Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Fisher Linear Discriminant Analysis (LDA) is a widely-used projection technique. Its application includes face recognition and speaker recognition. The kernel version of LDA (KDA) has also been developed, which generalizes LDA by introducing a kernel. LDA and KDA consists of a within-class scatter matrix and a between-class scatter matrix. The original formulations of LDA and KDA involve the inversion of the within-class scatter matrix, which may have singularity problem. A simple way to prevent singularity is adding a regularization term to the within-class scatter matrix. The resulting LDA and KDA are called Regularized LDA (RLDA) and Regularized KDA (RKDA). In this paper, we experimentally investigate how this regularization term will influence the performance of LDA and KDA. In addition, we introduce an extra regularization term to the between-class scatter matrix, and the resulting LDA and KDA are then called Doubly Regularized LDA (D-RLDA) and Doubly Regularized KDA (D-RKDA). We then apply LDA, KDA, RLDA, RKDA, D-RLDA and D-RKDA as a feature projection technique to two audio signal classification tasks. Gaussian Supervector (GSV) is used as the feature vector and linear Support Vector Machine (SVM) is used as the classifier. Experimental results show that, RLDA, D-RLDA, RKDA and D- RKDA are more effective than the conventional LDA and KDA. Besides, D-RLDA and D-RKDA are more robust than RLDA and RKDA.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 8 Jul 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • audio signal classification
  • double regularization
  • Fisher linear discriminant analysis
  • kernel Fisher discriminant analysis

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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