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Laplacian Eigenmaps for dimensionality reduction and data representation
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The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
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One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
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IEEE Transactions on Knowledge and Data Engineering
Image Feature Extraction Using Gradient Local Auto-Correlations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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ACM Transactions on Intelligent Systems and Technology (TIST)
Pattern Recognition Letters
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Semi-supervised learning effectively integrates labeled and unlabeled samples for classification, and most of the methods are founded on the pair-wise similarities between the samples. In this paper, we propose methods to construct similarities from the probabilistic viewpoint, whilst the similarities have so far been formulated in a heuristic manner such as by k-NN. We first propose the kernel-based formulation of transition probabilities via considering kernel least squares in the probabilistic framework. The similarities are consequently derived from the kernel-based transition probabilities which are efficiently computed, and the similarities are inherently sparse without applying k-NN. In the case of multiple types of kernel functions, the multiple transition probabilities are also obtained correspondingly. From the probabilistic viewpoint, they can be integrated with prior probabilities, i.e., linear weights, and we propose a computationally efficient method to optimize the weights in a discriminative manner, as in multiple kernel learning. The novel similarity is thereby constructed by the composite transition probability and it benefits the semi-supervised learning methods as well. In the various experiments on semi-supervised learning problems, the proposed methods demonstrate favorable performances, compared to the other methods, in terms of classification performances and computation time.