A classification scheme for 'high-dimensional-small-sample-size' data using soda and ridge-SVM with microwave measurement applications

Yinan Yu, Tomas McKelvey, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

The generalization performance of SVM-type classifiers severely suffers from the 'curse of dimensionality'. For some real world applications, the dimensionality of the measurement is sometimes significantly larger compared to the amount of training data samples available. In this paper, a classification scheme is proposed and compared with existing techniques for such scenarios. The proposed scheme includes two parts: (i) feature selection and transformation based on Fisher discriminant criteria and (ii) a hybrid classifier combining Kernel Ridge Regression with Support Vector Machine to predict the label of the data. The first part is named Successively Orthogonal Discriminant Analysis (SODA), which is applied after Fisher score based feature selection as a preliminary processing for dimensionality reduction. At this step, SODA maximizes the ratio of between-class-scatter and within-class-scatter to obtain an orthogonal transformation matrix which maps the features to a new low dimensional feature space where the class separability is maximized. The techniques are tested on high dimensional data from a microwave measurements system and are compared with existing techniques.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3542-3546
Number of pages5
DOIs
StatePublished - Oct 18 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Feature extraction
  • Microwave measurements
  • Ridge-SVM
  • SODA

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    Yu, Y., McKelvey, T., & Kung, S. Y. (2013). A classification scheme for 'high-dimensional-small-sample-size' data using soda and ridge-SVM with microwave measurement applications. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 3542-3546). [6638317] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638317