Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/155172
Title: Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning
Authors: Nannan Gu;Mingyu Fan;Deyu Meng
subject: manifold regularization (MR)|semi-supervised learning|Locally linear reconstruction (LLR)|self-paced learning (SPL)|semi-supervised classification (SSC)
Year: 2016
Publisher: IEEE
Abstract: Data labeling is a tedious and subjective task that can be time consuming and error-prone; however, most learning algorithms are sensitive to noisy labels. This problem raises the need to develop algorithms that can exploit large amount of unlabeled data and also be robust to noisy label information. In this letter, we propose a novel semi-supervised classification framework that is robust to noisy labels, named self-paced manifold regularization. The proposed framework naturally integrates self-paced learning regime into the manifold regularization framework for selecting labeled training samples in a theoretically sound manner, and utilizes locally linear reconstructions to control the smoothness of the classifier with respect to the manifold structure of data. Finally, the alternative search strategy is adopted for the proposed framework to obtain the classifier. The proposed method can not only suppress the negative effect of noisy initial labels in semi-supervised learning, but also obtain an explicit multiclass classifier for newly coming data points. Experimental results demonstrate the effectiveness of the proposed method.
URI: http://localhost/handle/Hannan/155172
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
More Information: 1806
1810
Appears in Collections:2016

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Title: Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning
Authors: Nannan Gu;Mingyu Fan;Deyu Meng
subject: manifold regularization (MR)|semi-supervised learning|Locally linear reconstruction (LLR)|self-paced learning (SPL)|semi-supervised classification (SSC)
Year: 2016
Publisher: IEEE
Abstract: Data labeling is a tedious and subjective task that can be time consuming and error-prone; however, most learning algorithms are sensitive to noisy labels. This problem raises the need to develop algorithms that can exploit large amount of unlabeled data and also be robust to noisy label information. In this letter, we propose a novel semi-supervised classification framework that is robust to noisy labels, named self-paced manifold regularization. The proposed framework naturally integrates self-paced learning regime into the manifold regularization framework for selecting labeled training samples in a theoretically sound manner, and utilizes locally linear reconstructions to control the smoothness of the classifier with respect to the manifold structure of data. Finally, the alternative search strategy is adopted for the proposed framework to obtain the classifier. The proposed method can not only suppress the negative effect of noisy initial labels in semi-supervised learning, but also obtain an explicit multiclass classifier for newly coming data points. Experimental results demonstrate the effectiveness of the proposed method.
URI: http://localhost/handle/Hannan/155172
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
More Information: 1806
1810
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7600403.pdf345.56 kBAdobe PDFThumbnail
Preview File
Title: Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning
Authors: Nannan Gu;Mingyu Fan;Deyu Meng
subject: manifold regularization (MR)|semi-supervised learning|Locally linear reconstruction (LLR)|self-paced learning (SPL)|semi-supervised classification (SSC)
Year: 2016
Publisher: IEEE
Abstract: Data labeling is a tedious and subjective task that can be time consuming and error-prone; however, most learning algorithms are sensitive to noisy labels. This problem raises the need to develop algorithms that can exploit large amount of unlabeled data and also be robust to noisy label information. In this letter, we propose a novel semi-supervised classification framework that is robust to noisy labels, named self-paced manifold regularization. The proposed framework naturally integrates self-paced learning regime into the manifold regularization framework for selecting labeled training samples in a theoretically sound manner, and utilizes locally linear reconstructions to control the smoothness of the classifier with respect to the manifold structure of data. Finally, the alternative search strategy is adopted for the proposed framework to obtain the classifier. The proposed method can not only suppress the negative effect of noisy initial labels in semi-supervised learning, but also obtain an explicit multiclass classifier for newly coming data points. Experimental results demonstrate the effectiveness of the proposed method.
URI: http://localhost/handle/Hannan/155172
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
More Information: 1806
1810
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7600403.pdf345.56 kBAdobe PDFThumbnail
Preview File