Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training support vector machines with multiple equality constraints
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multi-view kernel machine on single-view data
Neurocomputing
Multiple Kernel learning using regularized Ho-Kashyap classifier in empirical Kernel mapping space
ICNC'09 Proceedings of the 5th international conference on Natural computation
SSPS: A Semi-Supervised Pattern Shift for Classification
Neural Processing Letters
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
A novel multi-view classifier based on Nyström approximation
Expert Systems with Applications: An International Journal
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Maximum margin discriminant analysis (MMDA) was proposed that uses the margin idea for feature extraction. It often outperforms traditional methods like kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in other kernel methods, its time complexity is cubic in the number of training points m, and is thus computationally inefficient on massive data sets. In this paper, we propose an (1+ε)2-approximation algorithm for obtaining the MMDA features by extending the core vector machines. The resultant time complexity is only linear in m, while its space complexity is independent of m. Extensive comparisons with the original MMDA, KPCA, and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy, and is also faster than these kernel-based methods by more than an order of magnitude.