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Noise-robust semi-supervised learning via fast sparse coding
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DOI：10.1016/j.patcog.2014.08.019.png)
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This paper presents a novel noise-robust graph-based semi-supervised learning algorithm to deal with the challenging problem of semi-supervised learning with noisy initial labels. Inspired by the successful use of sparse coding for noise reduction, we choose to give new L-1-norm formulation of Laplacian regularization for graph-based semi-supervised learning. Since our L-1-norm Laplacian regularization is explicitly defined over the eigenvectors of the normalized Laplacian matrix, we formulate graph-based semi-supervised learning as an L-1-norm linear reconstruction problem which can be efficiently solved by sparse coding. Furthermore, by working with only a small subset of eigenvectors, we develop a fast sparse coding algorithm for our L-1-norm semi-supervised learning. Finally, we evaluate the proposed algorithm in noise-robust image classification. The experimental results on several benchmark datasets demonstrate the promising performance of the proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Graph-based semi-supervised learning
Noise reduction
Laplacian regularization
Sparse coding
Noise-robust image classification
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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Image denoising via sparse and redundant representations over learned dictionaries通过学习字典上的稀疏和冗余表示进行图像去噪
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