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The graph based semi-supervised algorithm with l1-regularizer
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DOI：10.1016/j.neucom.2014.07.037.png)
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Abstract
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In this paper a new graph-based semi-supervised algorithm for regression problem is proposed. An excess generalization error bound is established. It evaluates the learning performance of the proposed method and has a fast convergence rate with O(l(epsilon-1)) decay. An example is given to show that the proposed method uses a small portion of the labeled and unlabeled data to represent the target function, which illustrates the sparsity of the algorithm, and can efficiently reduce the computational complexity of the semi-supervised learning. Moreover, some experiments are performed to validate the sparsity and learning performance of the formulation. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Graph-based semi-supervised learning
l(1)-regularizer
Excess misclassification error
Hypothesis error
Manifold error
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