Projected gradient methods for linearly constrained problems
Mathematical Programming: Series A and B
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Feature Selection for Local Learning Based Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Greedy unsupervised multiple kernel learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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For most kernel-based clustering algorithms, their performance will heavily hinge on the choice of kernel. In this paper, we propose a novel kernel learning algorithm within the framework of the Local Learning based Clustering (LLC) (Wu & Schölkopf 2006). Given multiple kernels, we associate a non-negative weight with each Hilbert space for the corresponding kernel, and then extend our previous work on feature selection (Zeng & Cheung 2009) to select the suitable Hilbert spaces for LLC. We show that it naturally renders a linear combination of kernels. Accordingly, the kernel weights are estimated iteratively with the local learning based clustering. The experimental results demonstrate the effectiveness of the proposed algorithm on the benchmark document datasets.