Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
The Journal of Machine Learning Research
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Learning neighborhoods for metric learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Multi-metric learning for multi-sensor fusion based classification
Information Fusion
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In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pair-wise (dis-)similarity constraints, we learn a kernel matrix over the data that respects the provided side-information as well as the local geometry of the data. Our framework is based on metric learning methods, where we jointly model the metric/kernel over the data along with the underlying manifold. Furthermore, we show that for some important parameterized forms of the underlying manifold model, we can estimate the model parameters and the kernel matrix efficiently. Our resulting algorithm is able to incorporate local geometry into the metric learning task; at the same time it can handle a wide class of constraints. Finally, our algorithm is fast and scalable -- unlike most of the existing methods, it is able to exploit the low dimensional manifold structure and does not require semi-definite programming. We demonstrate wide applicability and effectiveness of our framework by applying to various machine learning tasks such as semi-supervised classification, colored dimensionality reduction, manifold alignment etc. On each of the tasks our method performs competitively or better than the respective state-of-the-art method.