Enhanced Supervised Principal Component Analysis for Cancer Classification

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...

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Bibliographic Details
Published inIraqi journal of science pp. 1321 - 1333
Main Authors Mahdi, Ghadeer JM, Kalaf, Bayda A., Khaleel, Mundher A.
Format Journal Article
LanguageEnglish
Published 30.04.2021
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Summary: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.
ISSN:0067-2904
2312-1637
DOI:10.24996/ijs.2021.62.4.28