Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A survey of kernel and spectral methods for clustering
Pattern Recognition
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The global kernel k-means algorithm for clustering in feature space
IEEE Transactions on Neural Networks
Kernel Learning for Local Learning Based Clustering
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
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Multiple kernel learning (MKL) has emerged as a powerful tool for considering multiple kernels when the appropriate representation of the data is unknown. Some of these kernels may be complementary, while others irrelevant to the learning task. In this work we present an MKL method for clustering. The intra-cluster variance objective is extended by learning a linear combination of kernels, together with the cluster labels, through an iterative procedure. Closed-form updates for the combination weights are derived, that greatly simplify the optimization. Moreover, to allow for robust kernel mixtures, a parameter that regulates the sparsity of the weights is incorporated into our framework. Experiments conducted on a collection of images reveal the effectiveness of the proposed method.