Partial Multi-label Learning Based On Near-Far Neighborhood Label Enhancement And Nonlinear Guidance
Authors: 
Yu Chen, Yanan Wu, Na Han, Xiaozhao Fang, Bingzhi Chen, Jie WenAuthors Info & Claims
Yu Chen, Yanan Wu, Na Han, Xiaozhao Fang, Bingzhi Chen, Jie WenAuthors Info & ClaimsPages 3722 - 3731
Abstract
Partial multi-label learning (PML) deals with the problem of accurately predicting the correct multi-label class for each instance in multi-label data containing noise. Compared with traditional multi-label learning, partial multi-label learning requires learning and completing multi-label classification tasks in an imperfect environment. The existing PML methods have the following problems: (1) the correlation between samples and labels is not fully utilized; (2) the nonlinear nature of the model is not taken into account. To solve these problems, we propose a new method of PML based on label enhancement of near and far neighbor information and nonlinear guidance(PML-LENFN). Specifically, the original binary label information is reconstructed by using the information of sample near neighbors and far neighbors to eliminate the influence of noise. Then we construct a linear multi-label classifier that can explore label correlation. In order to learn the nonlinear relationship between features and labels, we use nonlinear mapping to constrain this classifier, so as to obtain the prediction results that are more consistent with the realistic label distribution.
References
[1]
Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor W Tsang. The emerging trends of multi-label learning. IEEE transactions on pattern analysis and machine intelligence, 44(11):7955--7974, 2021.
[2]
Shahab Tahzeeb and S. M. Mamun Hasan. A neural network-based multi-label classifier for protein function prediction. Engineering, Technology & Applied Science Research, 2022.
[3]
Foteini Markatopoulou, Vasileios Mezaris, and Ioannis Patras. Implicit and explicit concept relations in deep neural networks for multi-label video/image annotation. IEEE transactions on circuits and systems for video technology, 29(6):1631--1644, 2018.
[4]
Xiao Ke, Jiawei Zou, and Yuzhen Niu. End-to-end automatic image annotation based on deep cnn and multi-label data augmentation. IEEE Transactions on Multimedia, 21(8):2093--2106, 2019.
[5]
André Elisseeff and Jason Weston. A kernel method for multi-labelled classification. Advances in neural information processing systems, 14, 2001.
[6]
Ming-Kun Xie and Sheng-Jun Huang. Partial multi-label learning. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[7]
Min-Ling Zhang and Zhi-Hua Zhou. Ml-knn: A lazy learning approach to multilabel learning. Pattern recognition, 40(7):2038--2048, 2007.
[8]
Min-Ling Zhang and LeiWu. Lift: Multi-label learning with label-specific features. IEEE transactions on pattern analysis and machine intelligence, 37(1):107--120, 2014.
[9]
Lijuan Sun, Songhe Feng, Jun Liu, Gengyu Lyu, and Congyan Lang. Global-local label correlation for partial multi-label learning. IEEE Transactions on Multimedia, 24:581--593, 2021.
[10]
Min-Ling Zhang and Jun-Peng Fang. Partial multi-label learning via credible label elicitation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10):3587--3599, 2020.
[11]
Bing-Qing Liu, Bin-Bin Jia, and Min-Ling Zhang. Towards enabling binary decomposition for partial multi-label learning. IEEE transactions on pattern analysis and machine intelligence, 2023.
[12]
Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu, and Gang Chen. Discriminative and correlative partial multi-label learning. In IJCAI, pages 3691-- 3697, 2019.
[13]
Ning Xu, Yun-Peng Liu, and Xin Geng. Partial multi-label learning with label distribution. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 6510--6517, 2020.
[14]
Ning Xu, Yun-Peng Liu, Yan Zhang, and Xin Geng. Progressive enhancement of label distributions for partial multilabel learning. IEEE Transactions on Neural Networks and Learning Systems, 34(8):4856--4867, 2021.
[15]
Lijuan Sun, Songhe Feng, Tao Wang, Congyan Lang, and Yi Jin. Partial multilabel learning by low-rank and sparse decomposition. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 5016--5023, 2019.
[16]
Ziwei Li, Gengyu Lyu, and Songhe Feng. Partial multi-label learning via multisubspace representation. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 2612--2618, 2021.
[17]
Ming-Kun Xie, Feng Sun, and Sheng-Jun Huang. Partial multi-label learning with meta disambiguation. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 1904--1912, 2021.
[18]
Matthew R Boutell, Jiebo Luo, Xipeng Shen, and Christopher M Brown. Learning multi-label scene classification. Pattern recognition, 37(9):1757--1771, 2004.
[19]
Boyuan Zhang, Zheming Li, Landong Liu, and Zhenwu Wang. Landmark-based partial multi-label learning with noise processing. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1--8. IEEE, 2023.
[20]
Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, and Klaus Brinker. Multilabel classification via calibrated label ranking. Machine learning, 73:133-- 153, 2008.
[21]
Peng Hou, Xin Geng, and Min-Ling Zhang. Multi-label manifold learning. In Proceedings of the AAAI conference on artificial intelligence, volume 30, 2016.
[22]
Jia Zhang, Zhiming Luo, Candong Li, Changen Zhou, and Shaozi Li. Manifold regularized discriminative feature selection for multi-label learning. Pattern Recognition, 95:136--150, 2019.
[23]
Tianna Zhao, Yuanjian Zhang, and Witold Pedrycz. Robust multi-label classification with enhanced global and local label correlation. Mathematics, 10(11):1871, 2022.
[24]
Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, and Yong Xu. Deep double incomplete multi-view multi-label learning with incomplete labels and missing views. IEEE transactions on neural networks and learning systems, 2023.
[25]
Qingyang Zhang, Yake Wei, Zongbo Han, Huazhu Fu, Xi Peng, Cheng Deng, Qinghua Hu, Cai Xu, Jie Wen, Di Hu, et al. Multimodal fusion on low-quality data: A comprehensive survey. arXiv preprint arXiv:2404.18947, 2024.
[26]
JieWen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(2):1136--1149, 2022.
[27]
Timothee Cour, Ben Sapp, and Ben Taskar. Learning from partial labels. The Journal of Machine Learning Research, 12:1501--1536, 2011.
[28]
Yves Grandvalet and Yoshua Bengio. Learning from partial labels with minimum entropy. 2004.
[29]
Min-Ling Zhang, Fei Yu, and Cai-Zhi Tang. Disambiguation-free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 29(10):2155--2167, 2017.
[30]
Eyke Hüllermeier and Jürgen Beringer. Learning from ambiguously labeled examples. Intelligent Data Analysis, 10(5):419--439, 2006.
[31]
Min-Ling Zhang and Fei Yu. Solving the partial label learning problem: An instance-based approach. In IJCAI, pages 4048--4054, 2015.
[32]
Rong Jin and Zoubin Ghahramani. Learning with multiple labels. Advances in neural information processing systems, 15, 2002.
[33]
Liping Liu and Thomas Dietterich. A conditional multinomial mixture model for superset label learning. Advances in neural information processing systems, 25, 2012.
[34]
Fei Yu and Min-Ling Zhang. Maximum margin partial label learning. In Asian conference on machine learning, pages 96--111. PMLR, 2016.
[35]
Xiang Fang, Zeyu Xiong, Wanlong Fang, Xiaoye Qu, Chen Chen, Jianfeng Dong, Keke Tang, Pan Zhou, Yu Cheng, and Daizong Liu. Rethinking weakly-supervised video temporal grounding from a game perspective. In European Conference on Computer Vision. Springer, 2024.
[36]
Min-Ling Zhang and Jun-Peng Fang. Partial multi-label learning via credible label elicitation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10):3587--3599, 2021.
[37]
Guoxian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang, Zhao Li, Zili Zhang, and Xindong Wu. Feature-induced partial multi-label learning. In 2018 IEEE international conference on data mining (ICDM), pages 1398--1403. IEEE, 2018.
[38]
Ming-Kun Xie and Sheng-Jun Huang. Partial multi-label learning with noisy label identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7):3676--3687, 2021.
[39]
Gengyu Lyu, Songhe Feng, and Yidong Li. Partial multi-label learning via probabilistic graph matching mechanism. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 105--113, 2020.
[40]
Peng Zhao, Shiyi Zhao, Xuyang Zhao, Huiting Liu, and Xia Ji. Partial multilabel learning based on sparse asymmetric label correlations. Knowledge-Based Systems, 245:108601, 2022.
[41]
Yan Hu, Xiaozhao Fang, Peipei Kang, Yonghao Chen, Yuting Fang, and Shengli Xie. Dual noise elimination and dynamic label correlation guided partial multi-label learning. IEEE Transactions on Multimedia, 2023.
[42]
Xiang Fang, Daizong Liu, Pan Zhou, and Guoshun Nan. You can ground earlier than see: An effective and efficient pipeline for temporal sentence grounding in compressed videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2448--2460, 2023.
[43]
Xiang Fang, Daizong Liu, Wanlong Fang, Pan Zhou, Zichuan Xu, Wenzheng Xu, Junyang Chen, and Renfu Li. Fewer steps, better performance: Efficient crossmodal clip trimming for video moment retrieval using language. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 1735--1743, 2024.
[44]
Yan Hu, Xiaozhao Fang, Weijun Lv, and Peipei Kang. Partial multi-label learning: exploration of binary ground-truth labels. In 2023 IEEE International Conference on Multimedia and Expo (ICME), pages 1811--1816. IEEE, 2023.
[45]
Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373--1396, 2003.
[46]
Xi Peng, Canyi Lu, Zhang Yi, and Huajin Tang. Connections between nuclearnorm and frobenius-norm-based representations. IEEE transactions on neural networks and learning systems, 29(1):218--224, 2016.
[47]
ChangzhongWang, YanWang, Tingquan Deng, andWeiping Ding. Missing multilabel learning based on the fusion of two-level nonlinear mappings. Information Fusion, 103:102105, 2024.
[48]
K. M. Muraleedharan, K. T. Bibish Kumar, Sunil John, and R. K. Sunil Kumar. Combined use of nonlinear measures for analyzing pathological voices. International Journal of Image and Graphics, 24(03):2450035, 2024.
[49]
Tingquan Deng, Yang Huang, Ge Yang, and Changzhong Wang. Pointwise mutual information sparsely embedded feature selection. International Journal of Approximate Reasoning, 151:251--270, 2022.
[50]
Lei Feng, Jun Huang, Senlin Shu, and Bo An. Regularized matrix factorization for multilabel learning with missing labels. IEEE transactions on cybernetics, 52(5):3710--3721, 2020.
[51]
Mark J Huiskes and Michael S Lew. The mir flickr retrieval evaluation. In Proceedings of the 1st ACM international conference on Multimedia information retrieval, pages 39--43, 2008.
[52]
Sotiris Diplaris, Grigorios Tsoumakas, Pericles A Mitkas, and Ioannis Vlahavas. Protein classification with multiple algorithms. In Advances in Informatics: 10th Panhellenic Conference on Informatics, PCI 2005, Volas, Greece, November 11--13, 2005. Proceedings 10, pages 448--456. Springer, 2005.
[53]
D Torres. Semantic annotation and retrieval of music and sound effects. IEEE Trans. on Audio, Speech, and Language Processing, 16(2), 2008.
[54]
Ioannis Katakis, Grigorios Tsoumakas, and Ioannis Vlahavas. Multilabel text classification for automated tag suggestion. ECML PKDD discovery challenge, 75:2008, 2008.
[55]
Cees GM Snoek, Marcel Worring, Jan C Van Gemert, Jan-Mark Geusebroek, and Arnold WM Smeulders. The challenge problem for automated detection of 101 semantic concepts in multimedia. In Proceedings of the 14th ACM international conference on Multimedia, pages 421--430, 2006.
[56]
Forrest Briggs, Balaji Lakshminarayanan, Lawrence Neal, Xiaoli Z Fern, Raviv Raich, Sarah JK Hadley, Adam S Hadley, and Matthew G Betts. Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach. The Journal of the Acoustical Society of America, 131(6):4640--4650, 2012.
[57]
Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, Ioannis P Vlahavas, et al. Multi-label classification of music into emotions. In ISMIR, volume 8, pages 325--330, 2008.
[58]
Min-Ling Zhang and Zhi-Hua Zhou. A review on multi-label learning algorithms. IEEE transactions on knowledge and data engineering, 26(8):1819--1837, 2013.
[59]
Sheng-Jun Huang, Wei Gao, and Zhi-Hua Zhou. Fast multi-instance multilabel learning. IEEE transactions on pattern analysis and machine intelligence, 41(11):2614--2627, 2018.
Index Terms
- Partial Multi-label Learning Based On Near-Far Neighborhood Label Enhancement And Nonlinear Guidance
Recommendations
Noisy Label Removal for Partial Multi-Label Learning
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningThis paper addresses the problem of partial multi-label learning (PML), a challenging weakly supervised learning framework, where each sample is associated with a candidate label set comprising both ground-true labels and noisy labels. We theoretically ...
Few-shot partial multi-label learning with synthetic features network
AbstractIn partial multi-label learning (PML) problems, each training sample is partially annotated with a candidate label set, among which only a subset of labels are valid. The major hardship for PML is that its training procedure is prone to be misled ...
Partial multi-label learning based on sparse asymmetric label correlations
AbstractIn many real-world applications, an instance from the training dataset of multi-label learning (MLL) often has some irrelevant labels. Traditional MLL and partial label learning (PLL) cannot deal with this problem very well. This has given rise ...
Comments
Information & Contributors
Information
Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 October 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
MM '24
Sponsor:
MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
Acceptance Rates
MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 148Total Downloads
- Downloads (Last 12 months)148
- Downloads (Last 6 weeks)72
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in