Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
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Recently, multiple kernel learning (MKL) has gained increasing attention due to its empirical superiority over traditional single kernel based methods. However, most of state-of-the-art MKL methods are ''uniform'' in the sense that the relative weights of kernels keep fixed among all data. Here we propose a ''non-uniform'' MKL method with a data-dependent gating mechanism, i.e., adaptively determine the kernel weights for the samples. We utilize a soft clustering algorithm and then tune the weight for each cluster under the graph embedding (GE) framework. The idea of exploiting cluster structures is based on the observation that data from the same cluster tend to perform consistently, which thus increases the resistance to noises and results in more reliable estimate. Moreover, it is computationally simple to handle out-of-sample data, whose implicit RKHS representations are modulated by the posterior to each cluster. Quantitative studies between the proposed method and some representative MKL methods are conducted on both synthetic and widely used public data sets. The experimental results well validate its superiorities.