Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning the unified kernel machines for classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SimpleNPKL: simple non-parametric kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Low-Rank Kernel Learning with Bregman Matrix Divergences
The Journal of Machine Learning Research
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
Spectral kernel learning for semi-supervised classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
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A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold Regularized Least-Squares (RLS) to learn non-parametric kernels efficiently. The proposed algorithm enjoys a closed-form solution in each iteration, which can be efficiently computed by the Lanczos sparse eigen-decomposition technique. Meanwhile, it can be extended to supervised kernel learning naturally. Experimental results show that our proposed unsupervised non-parametric kernel learning algorithm is significantly more effective and applicable to enhance the performance of Maximum Margin Clustering (MMC). Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings.