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.
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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
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DOI: https://doi.org/10.1007/978-3-319-75429-1_13
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