Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse and Semi-supervised Visual Mapping with the S^3GP
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
On semi-supervised kernel methods
On semi-supervised kernel methods
A manifold regularization approach to calibration reduction for sensor-network based tracking
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Semi-supervised learning for WLAN positioning
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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Classified labels are expensive by virtue of the utilization of field knowledge while the unlabeled data contains significant information, which can not be explored by supervised learning The Manifold Regularization (MR) based semi-supervised learning (SSL) could explores information from both labeled and unlabeled data Moreover, the model selection of MR seriously affects its predictive performance due to the inherent additional geometry regularizer of SSL In this paper, a leave-one-out cross-validation based PRESS criterion is first presented for model selection of MR to choose appropriate regularization coefficients and kernel parameters The Manifold regularization and model selection algorithm are employed to a real-life benchmark dataset The proposed approach, leveraged by effectively exploiting the embedded intrinsic geometric manifolds, outperforms the original MR and supervised learning approaches.