Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Improved kernel fisher discriminant analysis for fault diagnosis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The early detection and reliable diagnosis of a fault is crucial in an on-going operation of processes. They provide early warning for a fault and identification of its assignable cause. This paper proposes a classification tree-based diagnosis scheme combined with nonlinear kernel discriminant analysis. The nonlinear kernel-based dimension reduction for the discrimination of various classes of data is performed to determine nonlinear decision boundaries. The use of the nonlinear kernel method in a classification tree is to reduce the dimension of data and to provide its lower-dimensional representation suitable for separating different classes. We also present the use of orthogonal filter as a preprocessing step. An orthogonal filter-based preprocessing is performed to remove unwanted variation of data for enhancing discrimination power and classification performance. The performance of the proposed method is demonstrated using simulation data and compared with other methods. The classification results showed that the proposed tree-based method outperforms traditional PCA-based method.