Polarization Radar HRRP Recognition Based on Kernel Methods
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Improved wavelet feature extraction using kernel analysis for text independent speaker recognition
Digital Signal Processing
3D reconstruction and face recognition using kernel-based ICA and neural networks
Expert Systems with Applications: An International Journal
Optimized discriminative transformations for speech features based on minimum classification error
Pattern Recognition Letters
| Hi-index | 35.69 |
Kernel-based nonlinear feature extraction and classification algorithms are a popular new research direction in machine learning. This paper examines their applicability to the classification of phonemes in a phonological awareness drilling software package. We first give a concise overview of the nonlinear feature extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), kernel linear discriminant analysis (KLDA), and kernel springy discriminant analysis (KSDA). The overview deals with all the methods in a unified framework, regardless of whether they are unsupervised or supervised. The effect of the transformations on a subsequent classification is tested in combination with learning algorithms such as Gaussian mixture modeling (GMM), artificial neural nets (ANN), projection pursuit learning (PPL), decision tree-based classification (C4.5), and support vector machines (SVMs). We found, in most cases, that the transformations have a beneficial effect on the classification performance. Furthermore, the nonlinear supervised algorithms yielded the best results.