Abstract
Exploring and integrating data from heterogeneous sources have attracted much interest in recent years. However, one of the greatest challenges is that a lot of data are highly dimensional and diverse. In order to effectively combine multiple data sources, it is essential to reduce the number of dimensions and boost computational performance. This could be accomplished by combining multiple kernel learning with dimensionality reduction. In this paper, we propose an improved multiple kernel learning framework, referred to as fMKL-DR, that optimize equations to calculate matrix chain multiplication. To reach this conclusion, we performed several comparative evaluations on various biomedical data sets. The results demonstrate that, compared to previous work, the fMKL-DR remarkably improves computational cost. Therefore, the proposed framework is beneficial to the manipulation and integration of huge and complex datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kwon, Y.-J.: Genome-wide analysis of DNA methylation and the gene expression change in lung cancer. J. Thorac. Oncol. 7(1), 20–33 (2012)
Liang, M., Li, Z., Chen, T., Zeng, J.: Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 12(4), 928–937 (2015)
Wang, B.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014). https://doi.org/10.1038/nmeth.2810
Lin, Y.Y., Liu, T.L., Fuh, C.S.: Multiple kernel learning for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1147–1160 (2011)
Speicher, N.K., Pfeifer, N.: Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics 31(12), i268–i275 (2015). https://doi.org/10.1093/bioinformatics/btv244
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a General framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)
Vandenberghe, L., Boyd, S.: Semidefinite programming. SIAM Rev. 38(1), 49–95 (1996)
Shawe-Taylor, J., Mello, C.: Kernel method for Pattern Analysis. Cambridge University Press, Cambridge (2004)
The Cancer Genome Atlas. http://cancergenome.nih.gov
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Giang, T., Nguyen, T., Nguyen, T.Q.V., Tran, D. (2018). fMKL-DR: A Fast Multiple Kernel Learning Framework with Dimensionality Reduction. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-75429-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75428-4
Online ISBN: 978-3-319-75429-1
eBook Packages: Computer ScienceComputer Science (R0)