@inproceedings{283637d3ec7a408ab2dcf7ee68b6252e,
title = "A self-supervised learning framework for classifying microarray gene expression data",
abstract = "It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning frame-work for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.",
author = "Yijuan Lu and Qi Tian and Feng Liu and Maribel Sanchez and Yufeng Wang",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; ICCS 2006: 6th International Conference on Computational Science ; Conference date: 28-05-2006 Through 31-05-2006",
year = "2006",
doi = "10.1007/11758525_93",
language = "English (US)",
isbn = "3540343814",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "686--693",
booktitle = "Computational Science - ICCS 2006",
}