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For example, the multiple kernel learning (MKL) method can effectively combine multiple-channel features and then make a decision, since the multiple kernels in MKL can naturally correspond to features from different views [36].
De los Campos et al. [ 14] suggest an interesting venue for further research on the use of KRR for prediction in quantitative genetics, by combining multiple kernels in a single model, each kernel representing a single variance component (e.g., additive, dominance, or epistasis).
To obtain the local GEVs, one possible approach is to use a SPMM with multiple kernels in the form of y = X β + Z g 1 + Z g 2 + … + Z g k + e, (2 where g j ∼ N q k (0, σ g j 2 K j ) for j = 1, 2, …, k, e ∼ N n (0, σ e 2 I ), and g 1, …, g k, e are mutually independent.
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The use of multiple kernels results in improved model predictions.
To tackle the new problem, we propose a novel algorithm to incorporate multiple kernels and Laplacians in KL clustering.
> -wrap-foot> In this article, we propose a new clustering approach, OKLC, to optimize the combination of multiple kernels and Laplacians in clustering analysis.
We present the multi-wavelength study of short-term variations of Hα line emission located in multiple kernels on the both sides from magnetic neutral line in the 25th July 2004 solar flare observed by VTT (Tenerife).
Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings.
Our recent work proposes a method to integrate multiple kernel matrices in kernel k-means clustering (Yu,S. et al. Optimized data fusion for kernel K-means clustering, submitted for publication).
b In MKL framework, multiple kernels are used instead of a single one.
Hence, the recurrence is: In many applications, multiple kernels from different kernel functions or multiple sources of information are available.
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