The nature of statistical learning theory
The nature of statistical learning theory
Enhanced independent component analysis and its application to content based face image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Independent component analysis (ICA) is a feature extraction technique for blind source separation. Enhanced independent component analysis (EICA), which has enhanced generalization performance, operates in a reduced principal component analysis (PCA) space. SVM is a powerful supervised learning algorithm, which is rooted in statistical learning theory. SVM has demonstrated high generalization capabilities in many pattern recognition problems. In this paper, we integrate EICA with SVM and apply this new method to the leak detection problem in oil pipelines. In features extraction, EICA produces EICA features of the original pressure images. In classification, SVM classified the EICA features as leak or non-leak. The test results based on real data indicate that the method can detect many leak faults from a pressure curve, and reduce the ratio of false and missing alarm than conventional methods.