A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
A Vertical-Energy-Thresholding Procedure for Data Reduction With Multiple Complex Curves
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Fourier transform near-infrared (FT-NIR) technique is an effective approach to predict chemical properties and can be applied to online monitoring in bio-energy industry. High dimensionality and collinearity of the FT-NIR spectral data makes it difficult in some applications to construct the reliable prediction model. In this study, two nonlinear kernel methods with wavelet-compressed data, Kernel Partial Least Squares (KPLS) regression and Kernel Ridge Regression (KRR), are presented to resolve those data into a few predictors and then, more sophisticated models are created to capture the nonlinear relationships between the spectral data and concentrations determined by wet chemistry. A wavelet transform is adopted as a preprocessing procedure to reduce the data size for supporting real-time implementation of assessing biomass properties with FT-NIR spectroscopy. A real-life data of switchgrass is presented to illustrate the performance of the developed models and the results advocated that the use of nonlinear kernel procedure with wavelet compression improved the prediction performance of the model.