Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers
Neural Processing Letters
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A discriminant analysis using composite features for classification problems
Pattern Recognition
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Data-Dependent Kernel Machines for Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Discriminatively regularized least-squares classification
Pattern Recognition
A Criterion for Learning the Data-Dependent Kernel for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Gaussian kernel optimization for pattern classification
Pattern Recognition
Optimal Double-Kernel Combination for Classification
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Kernel Optimization Using a Generalized Eigenvalue Approach
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Sparse support vector regressors based on forward basis selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Subspace based linear programming support vector machines
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Subspace based least squares support vector machines for pattern classification
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Sparse kernel feature analysis using FastMap and its variants
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
Noise-based feature perturbation as a selection method for microarray data
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Analysis of the distance between two classes for tuning SVM hyperparameters
IEEE Transactions on Neural Networks
Representation of a fisher criterion function in a kernel feature space
IEEE Transactions on Neural Networks
Sparse least squares support vector regressors trained in the reduced empirical feature space
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A novel Gaussian kernel paramter choosing method
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Optimum kernel function design from scale space features for object detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
Block-wise 2D kernel PCA/LDA for face recognition
Information Processing Letters
Learning Translation Invariant Kernels for Classification
The Journal of Machine Learning Research
Similarity preserving principal curve: an optimal 1-D feature extractor for data representation
IEEE Transactions on Neural Networks
On Learning and Cross-Validation with Decomposed Nyström Approximation of Kernel Matrix
Neural Processing Letters
Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
General kernel optimization model based on kernel fisher criterion
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A new discriminant subspace analysis approach for multi-class problems
Pattern Recognition
A kernel optimization method based on the localized kernel fisher criterion
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A performance study of gaussian kernel classifiers for data mining applications
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Simultaneous clustering and classification over cluster structure representation
Pattern Recognition
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Evaluation measures for kernel optimization
Pattern Recognition Letters
Multiple kernel learning with gaussianity measures
Neural Computation
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Mathematical and Computer Modelling: An International Journal
Random projection ensemble learning with multiple empirical kernels
Knowledge-Based Systems
A novel method of sparse least squares support vector machines in class empirical feature space
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Kernelizing the proportional odds model through the empirical kernel mapping
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In this paper, we present a method of kernel optimization by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. Employing a data-dependent kernel, we derive an effective kernel optimization algorithm that maximizes the class separability of the data in the empirical feature space. It is shown that there exists a close relationship between the class separability measure introduced here and the alignment measure defined recently by Cristianini. Extensive simulations are carried out which show that the optimized kernel is more adaptive to the input data, and leads to a substantial, sometimes significant, improvement in the performance of various data classification algorithms.