Cited By
View all- Zhong JShang RZhao FZhang WXu S(2024)Negative Label and Noise Information Guided Disambiguation for Partial Multi-Label LearningIEEE Transactions on Multimedia10.1109/TMM.2024.340253426(9920-9935)Online publication date: 1-Jan-2024
Dual Noise Elimination and Dynamic Label Correlation Guided Partial Multi-Label Learning
Authors: 
Yan Hu, Xiaozhao Fang, Peipei Kang, Yonghao Chen, Yuting Fang, Shengli XieAuthors Info & Claims
Abstract
Partial multi-label learning (PML) needs to address the problem of multi-label learning when the dataset contains redundant information. PML is more challenging compared to traditional multi-label learning, because PML needs not only to perform the multi classification task, but also to reduce the impact of noise information on the model. Existing PML methods suffer from the following problems. (1) Only single source of noise is considered. (2) Some methods ignore the label correlations. To solve the above problems, we proposes a new dual noise elimination and dynamic label correlation guided partial multi-label learning (PML-DNDC). Specifically, the hidden ground-truth label matrix is decomposed into two compressed matrices of instance and classifier, which are used to approximate the candidate label matrix, to eliminate the negative effects of label noise on the model. On one hand, the compressed instance matrix maintains local structural consistency with the original instances, eliminating noise in the feature. On the other hand, dynamic label correlation guidance is designed to help classifier training by dynamically exploring the potential label correlations, which encourages relevant labels to obtain similar classifiers. After extensive experiments and analyses, we conclude that the proposed PML-DNDC is superior to the state-of-the-art methods.
References
[1]
W. Liu, H. Wang, X. Shen, and I. W. Tsang, “The emerging trends of multi-label learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 11, pp. 7955–7974, Nov. 2022.
[2]
A.N. Srivastava and B. Zane-Ulman, “Discovering recurring anomalies in text reports regarding complex space systems,” in Proc. IEEE Aerosp. Conf., 2005, pp. 3853–3862.
[3]
M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, “Learning multi-label scene classification,” Pattern Recognit., vol. 37, no. 9, pp. 1757–1771, 2004.
[4]
A. Elisseeff and J. Weston, “A Kernel method for multi-labelled classification,” Adv. Neural Inf. Process. Syst., vol. 14, pp. 681–687, 2001.
[5]
M.-K. Xie and S.-J. Huang, “Partial multi-label learning,” in Proc. AAAI Conf. Artif. Intell., vol. 32, no. 1, 2018, pp. 4302–4309.
[6]
M.-L. Zhang and Z.-H. Zhou, “ML-KNN: A lazy learning approach to multi-label learning,” Pattern Recognit., vol. 40, no. 7, pp. 2038–2048, 2007.
[7]
M.-L. Zhang and L. Wu, “Lift: Multi-label learning with label-specific features,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 1, pp. 107–120, Jan. 2015.
[8]
M.-L. Zhang and F. Yu, “Solving the partial label learning problem: An instance-based approach,” in Proc. 24th Int. Joint Conf. Artif. Intell., 2015, pp. 4048–4054.
[9]
L. Feng and B. An, “Partial label learning by semantic difference maximization,” in Proc. Int. Joint Conf. Artif. Intell., 2019, pp. 2294–2300.
[10]
M.-L. Zhang and J.-P. Fang, “Partial multi-label learning via credible label elicitation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 10, pp. 3587–3599, Oct. 2021.
[11]
G. Yu et al., “Feature-induced partial multi-label learning,” in Proc. IEEE Int. Conf. Data Mining, 2018, pp. 1398–1403.
[12]
T. Yu, G. Yu, J. Wang, C. Domeniconi, and X. Zhang, “Partial multi-label learning using label compression,” in Proc. IEEE Int. Conf. Data Mining, 2020, pp. 761–770.
[13]
G. Lyu, S. Feng, and Y. Li, “Noisy label tolerance: A new perspective of partial multi-label learning,” Inf. Sci., vol. 543, pp. 454–466, 2021.
[14]
M.-K. Xie and S.-J. Huang, “Partial multi-label learning with noisy label identification,” in Proc. Proc. AAAI Conf. Artif. Intell., 2020, pp. 6454–6461.
[15]
M.-L. Zhang, Y.-K. Li, X.-Y. Liu, and X. Geng, “Binary relevance for multi-label learning: An overview,” Front. Comput. Sci., vol. 12, no. 2, pp. 191–202, 2018.
[16]
M. Zhang, Y. K. Li, H. Yang, and X. Y. Liu, “Towards class-imbalance aware multi-label learning,” IEEE Trans. Cybern., vol. 52, no. 6, pp. 4459–4471, Jun. 2022.
[17]
A. Clare and R. D. King, “Knowledge discovery in multi-label phenotype data,” in Proc. Eur. Conf. Princ. Data Mining Knowl. Discov., 2001, pp. 42–53.
[18]
J. Fürnkranz, E. Hüllermeier, E. L. Mencía, and K. Brinker, “Multilabel classification via calibrated label ranking,” Mach. Learn., vol. 73, no. 2, pp. 133–153, 2008.
[19]
J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” Mach. Learn., vol. 85, no. 3, pp. 333–359, 2011.
[20]
L. Jian, J. Li, K. Shu, and H. Liu, “Multi-label informed feature selection,” in Proc. Int. Joint Conf. Artif. Intell., 2016, pp. 1627–33.
[21]
T. Cour, B. Sapp, and B. Taskar, “Learning from partial labels,” J. Mach. Learn. Res., vol. 12, pp. 1501–1536, 2011.
[22]
E. Hüllermeier and J. Beringer, “Learning from ambiguously labeled examples,” Intell. Data Anal., vol. 10, no. 5, pp. 419–439, 2006.
[23]
L. Liu and T. Dietterich, “A conditional multinomial mixture model for superset label learning,” Adv. Neural Inf. Process. Syst., vol. 25, pp. 557–565, 2012.
[24]
Y. Grandvalet and Y. Bengio, “Learning from partial labels with minimum entropy,” Centre interuniversitaire de recherche en analyse des organisations (CIRANO), 2004.
[25]
F. Yu and M.-L. Zhang, “Maximum margin partial label learning,” in Proc. Asian Conf. Mach. Learn., 2016, pp. 96–111.
[26]
Y. Rong, S. Xiong, and Y. Gao, “Discriminative dictionary pair learning from partially labeled data,” in Proc. IEEE Int. Conf. Image Process., 2016, pp. 719–723.
[27]
G. Lyu, S. Feng, T. Wang, C. Lang, and Y. Li, “GM-PLL: Graph matching based partial label learning,” IEEE Trans. Knowl. Data Eng., vol. 33, no. 2, pp. 521–535, Feb. 2021.
[28]
L. Sun, S. Feng, T. Wang, C. Lang, and Y. Jin, “Partial multi-label learning by low-rank and sparse decomposition,” in Proc. AAAI Conf. Artif. Intell., 2019, pp. 5016–5023.
[29]
M.-K. Xie and S.-J. Huang, “Partial multi-label learning with noisy label identification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 7, pp. 3676–3687, Jul. 2022.
[30]
L. Sun, S. Feng, J. Liu, G. Lyu, and C. Lang, “Global-local label correlation for partial multi-label learning,” IEEE Trans. Multimedia, vol. 24, pp. 581–593, 2022.
[31]
P. Zhao, S. Zhao, X. Zhao, H. Liu, and X. Ji, “Partial multi-label learning based on sparse asymmetric label correlations,” Knowl.-Based Syst., vol. 245, 2022, Art. no.
[32]
K. Konstantinides, B. Natarajan, and G. S. Yovanof, “Noise estimation and filtering using block-based singular value decomposition,” IEEE Trans. Image Process., vol. 6, no. 3, pp. 479–483, Mar. 1997.
[33]
M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput., vol. 15, no. 6, pp. 1373–1396, 2003.
[34]
Y. Zhu, J. T. Kwok, and Z.-H. Zhou, “Multi-label learning with global and local label correlation,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 6, pp. 1081–1094, Jun. 2018.
[35]
S. Diplaris, G. Tsoumakas, P. A. Mitkas, and I. Vlahavas, “Protein classification with multiple algorithms,” in Proc. Panhellenic Conf. Inform., 2005, pp. 448–456.
[36]
H. Wang, H. Huang, and C. Ding, “Image annotation using bi-relational graph of images and semantic labels,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2011, pp. 793–800.
[37]
R. H. Bartels and G. W. Stewart, “Solution of the matrix equation AX+XB= C,” Commun. ACM, vol. 15, no. 9, pp. 820–826, 1972.
[38]
G. Golub, S. Nash, and C. Van Loan, “A Hessenberg-Schur method for the problem AX+XB= C,” IEEE Trans. Autom. Control, vol. 24, no. 6, pp. 909–913, Dec. 1979.
[39]
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. Cambridge, MA, USA: MIT Press, 2018.
[40]
M.-K. Xie and S.-J. Huang, “CCMN: A general framework for learning with class-conditional multi-label noise,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 154–166, Jan. 2023.
[41]
A. Maurer, “A vector-contraction inequality for rademacher complexities,” in Proc. Algorithmic Learn. Theory: 27th Int. Conf., 2016, pp. 3–17.
[42]
W. Gao and Z. Zhou, “On the consistency of multi-label learning,” Artif. Intell., vol. 199–200, pp. 22–44, 2013.
[43]
M. J. Huiskes and M. S. Lew, “The MIR flickr retrieval evaluation,” in Proc. ACM Int. Conf. Multimedia Inf. Retrieval, 2008, pp. 39–43.
[44]
D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, “Semantic annotation and retrieval of music and sound effects,” IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 2, pp. 467–476, Feb. 2008.
[45]
I. Katakis, G. Tsoumakas, and I. Vlahavas, “Multilabel text classification for automated tag suggestion,” ECML PKDD Discovery Challenge, vol. 75, pp. 75–83, 2008.
[46]
C. G. Snoek, M. Worring, J. C. Van Gemert, J.-M. Geusebroek, and A. W. Smeulders, “The challenge problem for automated detection of 101 semantic concepts in multimedia,” in Proc. 14th ACM Int. Conf. Multimedia, 2006, pp. 421–430.
[47]
F. Briggs et al., “New methods for acoustic classification of multiple simultaneous bird species in a noisy environment,” in Proc. IEEE Int. Workshop Mach. Learn. Signal Process., 2013, pp. 1–8.
[48]
K. Trohidis et al., “Multi-label classification of music into emotions,” in Proc. ISMIR, 2008, pp. 325–330.
[49]
M.-L. Zhang and Z.-H. Zhou, “A review on multi-label learning algorithms,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 8, pp. 1819–1837, Aug. 2014.
[50]
J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” J. Mach. Learn. Res., vol. 7, pp. 1–30, 2006.
Recommendations
Label correlation guided discriminative label feature learning for multi-label chest image classification
Abstract Background and ObjectiveMulti-label Chest X-ray (CXR) images often contain rich label relationship information, which is beneficial to improve classification performance. However, because of the intricate relationships among labels, most ...
Highlights- An end-to-end multi-label learning framework is proposed for multi-label CXR image classification.
- Consistency loss between global and local label correlations help learns more accurate label correlation.
- Label correlation guided ...
Semi-supervised partial label learning algorithm via reliable label propagation
AbstractPartial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate label set. In PLL, when all possible candidate labels are as signed to real-world training examples, ...
Maximum margin partial label learning
Partial label learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the training example. The basic strategy to learn from partial label examples is disambiguation, i.e. by ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
1520-9210 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Press
Publication History
Published: 30 November 2023
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Nov 2024
Other Metrics
Citations
Cited By
View all- Zhong JShang RZhao FZhang WXu S(2024)Negative Label and Noise Information Guided Disambiguation for Partial Multi-Label LearningIEEE Transactions on Multimedia10.1109/TMM.2024.340253426(9920-9935)Online publication date: 1-Jan-2024
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in