Kernel ridge regression for general noise model with its application

The classical ridge regression technique makes an assumption that the noise is Gaussian. However, it is reported that the noise models in some practical applications do not satisfy Gaussian distribution, such as wind speed prediction. In this case, the classical regression techniques are not optimal. So we derive an optimal loss function and construct a new framework of kernel ridge regression technique for general noise model (N-KRR). The Augmented Lagrangian Multiplier method is introduced to solve N-KRR. We test the proposed technique on artificial data and short-term wind speed prediction. Experimental results confirm the effectiveness of the proposed model. HighlightsWe derive the optimal loss functions for different noise models.We develop a new framework of kernel ridge regression.We utilize the proposed model to short term wind speed prediction.The ALM approach is applied to solve the proposed model.