A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Matrix computations (3rd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
Kernel Methods for Measuring Independence
The Journal of Machine Learning Research
A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning
IEEE Transactions on Signal Processing
A test of independence based on a generalized correlation function
Signal Processing
Extraction of signals with specific temporal structure using kernel methods
IEEE Transactions on Signal Processing
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With the recent progress in kernel based learning methods, computation with Gram matrices has received immense attention. However, the complexity of computing the entire Gram matrix is quadratic in terms of number of samples. Therefore, a considerable amount of work has been focused on extracting relevant information from the Gram matrix without accessing all the elements. Most of these methods exploits the positive definiteness and rapidly decaying eigenstructure of the Gram matrix. Although information theoretic learning (ITL) is conceptually different from kernel based learning, several ITL estimators can be written in terms of Gram matrices. However, the difference between ITL and kernel based methods is that a few ITL estimators include a special type of matrix which is neither positive definite nor symmetric. In this paper we discuss how the techniques applied in kernel based learning can be applied to reduce computational complexity of the ITL estimators involving both Gram matrices and these other matrices.