Enhanced Supervised Principal Component Analysis for Cancer Classification

  • Ghadeer JM Mahdi Department of Mathematics, College of education for Pure Sciences- ibn Al-Haitham, University of Baghdad, Iraq https://orcid.org/0000-0003-4870-4034
  • Bayda A. Kalaf Department of Mathematics, College of education for Pure Sciences- ibn Al-Haitham, University of Baghdad, Iraq
  • Mundher A. Khaleel Department of Mathematics, Faculty of Computer Science and Mathematics, University of Tikrit, Iraq
Keywords: Classification, cancer diagnostic, Hilbert-Schmid, stochastic gradient descent, principal component analysis

Abstract

In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods.

Author Biography

Ghadeer JM Mahdi, Department of Mathematics, College of education for Pure Sciences- ibn Al-Haitham, University of Baghdad, Iraq

 

 

Published
2021-04-30
How to Cite
Mahdi, G. J., Kalaf , B. A., & Khaleel , M. A. (2021). Enhanced Supervised Principal Component Analysis for Cancer Classification. Iraqi Journal of Science, 62(4), 1321-1333. https://doi.org/10.24996/ijs.2021.62.4.28
Section
Mathematics