Semi-Supervised Learning via Geodesic Weighted Sparse Representation

Jianqiao WANG; Yuehua LI; Jianfei CHEN; Yuanjiang LI

doi:10.1587/transinf.E97.D.1673

Semi-Supervised Learning via Geodesic Weighted Sparse Representation

Jianqiao WANG, Yuehua LI, Jianfei CHEN, Yuanjiang LI

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The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.6 pp.1673-1676

Publication Date: 2014/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1673

Type of Manuscript: LETTER

Category: Pattern Recognition

Authors

Jianqiao WANG
  Nanjing University of Science and Technology
Yuehua LI
  Nanjing University of Science and Technology
Jianfei CHEN
  Nanjing University of Science and Technology
Yuanjiang LI
  Jiangsu University of Science and Technology

Keyword

label estimate, geodesic distance, sparse reconstruction, semi-supervised learning

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Jianqiao WANG, Yuehua LI, Jianfei CHEN, Yuanjiang LI, "Semi-Supervised Learning via Geodesic Weighted Sparse Representation" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 6, pp. 1673-1676, June 2014, doi: 10.1587/transinf.E97.D.1673.
Abstract: The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1673/_p

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@ARTICLE{e97-d_6_1673,
author={Jianqiao WANG, Yuehua LI, Jianfei CHEN, Yuanjiang LI, },
journal={IEICE TRANSACTIONS on Information},
title={Semi-Supervised Learning via Geodesic Weighted Sparse Representation},
year={2014},
volume={E97-D},
number={6},
pages={1673-1676},
abstract={The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.},
keywords={},
doi={10.1587/transinf.E97.D.1673},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Semi-Supervised Learning via Geodesic Weighted Sparse Representation
T2 - IEICE TRANSACTIONS on Information
SP - 1673
EP - 1676
AU - Jianqiao WANG
AU - Yuehua LI
AU - Jianfei CHEN
AU - Yuanjiang LI
PY - 2014
DO - 10.1587/transinf.E97.D.1673
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2014
AB - The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.
ER -