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In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.

- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.6 pp.1355-1361

- Publication Date
- 2020/06/01

- Publicized
- 2020/03/05

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2019EDP7182

- Type of Manuscript
- PAPER

- Category
- Artificial Intelligence, Data Mining

Tian XIE

Information Engineering University

Hongchang CHEN

China National Digital Switching System Engineering & Technological R&D Center

Tuosiyu MING

China National Digital Switching System Engineering & Technological R&D Center

Jianpeng ZHANG

China National Digital Switching System Engineering & Technological R&D Center

Chao GAO

China National Digital Switching System Engineering & Technological R&D Center

Shaomei LI

China National Digital Switching System Engineering & Technological R&D Center

Yuehang DING

China National Digital Switching System Engineering & Technological R&D Center

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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Tian XIE, Hongchang CHEN, Tuosiyu MING, Jianpeng ZHANG, Chao GAO, Shaomei LI, Yuehang DING, "Partial Label Metric Learning Based on Statistical Inference" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1355-1361, June 2020, doi: 10.1587/transinf.2019EDP7182.

Abstract: In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7182/_p

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@ARTICLE{e103-d_6_1355,

author={Tian XIE, Hongchang CHEN, Tuosiyu MING, Jianpeng ZHANG, Chao GAO, Shaomei LI, Yuehang DING, },

journal={IEICE TRANSACTIONS on Information},

title={Partial Label Metric Learning Based on Statistical Inference},

year={2020},

volume={E103-D},

number={6},

pages={1355-1361},

abstract={In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.},

keywords={},

doi={10.1587/transinf.2019EDP7182},

ISSN={1745-1361},

month={June},}

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TY - JOUR

TI - Partial Label Metric Learning Based on Statistical Inference

T2 - IEICE TRANSACTIONS on Information

SP - 1355

EP - 1361

AU - Tian XIE

AU - Hongchang CHEN

AU - Tuosiyu MING

AU - Jianpeng ZHANG

AU - Chao GAO

AU - Shaomei LI

AU - Yuehang DING

PY - 2020

DO - 10.1587/transinf.2019EDP7182

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E103-D

IS - 6

JA - IEICE TRANSACTIONS on Information

Y1 - June 2020

AB - In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.

ER -