Candidate Label-Aware Partial Label Learning Algorithm

Hongchang CHEN; Tian XIE; Chao GAO; Shaomei LI; Ruiyang HUANG

doi:10.11999/JEIT181059

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2516-2524

Hongchang CHEN, Tian XIE, Chao GAO, Shaomei LI, Ruiyang HUANG. Candidate Label-Aware Partial Label Learning Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2516-2524. doi: 10.11999/JEIT181059

Citation:

Hongchang CHEN, Tian XIE, Chao GAO, Shaomei LI, Ruiyang HUANG. Candidate Label-Aware Partial Label Learning Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2516-2524. doi: 10.11999/JEIT181059

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Candidate Label-Aware Partial Label Learning Algorithm

doi: 10.11999/JEIT181059

National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61601513)

Received Date: 2018-11-20
Rev Recd Date: 2019-04-21

Available Online: 2019-05-16

Publish Date: 2019-10-01

Abstract

Abstract

In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3%～16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2%～2.8%.
- Partial label learning,
- Weakly supervised learning,
- Disambiguation,
- Jaccard distance,
- Linear reconstruction

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References(19)

References

HÜLLERMEIER E and BERINGER J. Learning from ambiguously labeled examples[J]. Intelligent Data Analysis, 2006, 10(5): 419–439. doi: 10.3233/IDA-2006-10503

SONG Jingqi, LIU Hui, GENG Fenghuan, et al. Weakly-supervised classification of pulmonary nodules based on shape characters[C]. The 14th International Conference on Dependable, Autonomic and Secure Computing, The 14th International Conference on Pervasive Intelligence and Computing, The 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Auckland, New Zealand, 2016: 228–232.

TANG Caizhi and ZHANG Minling. Confidence-rated discriminative partial label learning[C]. The 31st AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017: 2611–2617.

TODA T, INOUE S, and UEDA N. Mobile activity recognition through training labels with inaccurate activity segments[C]. The 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Hiroshima, Japan, 2016: 57–64.

YU Fei and ZHANG Minling. Maximum margin partial label learning[J]. Machine Learning, 2017, 106(4): 573–593. doi: 10.1007/s10994-016-5606-4

LUO Jie and ORABONA F. Learning from candidate labeling sets[C]. The 23rd International Conference on Neural Information Processing Systems, Vancouver, Canada, 2010: 1504–1512.

ZHANG Minling and YU Fei. Solving the partial label learning problem: An instance-based approach[C]. The 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015: 4048–4054.

FENG Lei and AN Bo. Leveraging latent label distributions for partial label learning[C]. The Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018: 2107–2113.

COUR T, SAPP B, and TASKAR B. Learning from partial labels[J]. Journal of Machine Learning Research, 2011, 12: 1501–1536.

ZHOU Zhihua. A brief introduction to weakly supervised learning[J]. National Science Review, 2018, 5(1): 44–53. doi: 10.1093/nsr/nwx106

TOLDO R and FUSIELLO A. Robust multiple structures estimation with J-linkage[C]. The 10th European Conference on Computer Vision, Marseille, France, 2008: 537–547.

DUA D and TANISKIDOU E K. UCI machine learning repository[EB/OL]. http://archive.ics.uci.edu/ml, 2017.

ZENG Zinan, XIAO Shijie, JIA Kui, et al. Learning by associating ambiguously labeled images[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 708–715.

GUILLAUMIN M, VERBEEK J, and SCHMID C. Multiple instance metric learning from automatically labeled bags of faces[C]. The 11th European Conference on Computer Vision, Heraklion, Greece, 2010: 634–647.

ZHANG Minling, ZHOU Binbin, and LIU Xuying. Partial label learning via feature-aware disambiguation[C]. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2016: 1335–1344.

BRIGGS F, FERN X Z, and RAICH R. Rank-loss support instance machines for MIML instance annotation[C]. The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, 2012: 534–542.

LIU Liping and DIETTERICH T G. A conditional multinomial mixture model for superset label learning[C]. The 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012: 548–556.

ZHANG Minling, YU Fei, and TANG Caizhi. Disambiguation-free partial label learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2155–2167. doi: 10.1109/TKDE.2017.2721942

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