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A Classification Algorithm Based on Complex Number Feature
- Source :
- IEEE Access, Vol 8, Pp 17842-17853 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In this study, a classification algorithm based on complex number feature is proposed. Specifically, the SVM framework is reformulated, so each example would be classified in the unitary space. The cost function is redefined by considering the maximum margin of real and imaginary units of the complex number feature at the same time. The cost function is based on the expectation of the hinge loss, and its derivatives can be calculated in closed forms. Using a stochastic gradient descent (SGD) algorithm, this method allows for efficient implementation. For complex number feature, the example uncertainty is modeled by a sample preprocessing method based on within-class Euclidean distance Gaussian distribution sample (DGS). In addition, a complex number feature selection method based on improved hybrid discrimination analysis (HDA) is proposed by considering the correlation between real and imaginary units of complex number feature. The proposed classification algorithm is tested on synthetic data and three publicly available and popular datasets, namely, MNIST, WDBC, and Voc2012. Experimental results verify the effectiveness of the proposed method. The codes are available: https://github.com/luckysomebody/paper-code.
- Subjects :
- General Computer Science
Computer science
Gaussian
General Engineering
Feature selection
Function (mathematics)
Classification algorithm
Support vector machine
Euclidean distance
symbols.namesake
complex number feature
Stochastic gradient descent
feature selection
Feature (computer vision)
sample uncertainty
Hinge loss
symbols
feature fusion
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
Algorithm
Complex number
lcsh:TK1-9971
MNIST database
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....04bf9ca3d6588f36d166fea2dbbeea7f