Abstract
Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigenmatrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
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This research work was supported by Research Grants Council of Hong Kong under Grant No. 17301214 and HKU CERG Grants, Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China, Hung Hing Ying Physical Research Grant, and the Natural Science Foundation of China under Grant No. 11271144.
This paper was recommended for publication by Editor GAO Xiao-Shan.
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Jiang, H., Qiu, Y., Cheng, X. et al. On Eigen-matrix translation method for classification of biological data. J Syst Sci Complex 28, 1212–1230 (2015). https://doi.org/10.1007/s11424-015-3043-2
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DOI: https://doi.org/10.1007/s11424-015-3043-2