[1]QIN Tian,TENG Qifa,JIA Xiuyi.Weakly supervised label distribution learning by maintaining local label ranking[J].CAAI Transactions on Intelligent Systems,2023,18(1):47-55.[doi:10.11992/tis.202204018]
Copy

Weakly supervised label distribution learning by maintaining local label ranking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 47-55 Column: 学术论文—机器感知与模式识别 Public date: 2023-01-05
Title:
Weakly supervised label distribution learning by maintaining local label ranking
Author(s):
QIN Tian;  TENG Qifa;  JIA Xiuyi
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Keywords:
label distribution learning; label polysemy; weakly supervised learning; label ranking; weakly supervised label distribution learning; multi-label learning; label correlation; local label ranking relation
CLC:
TP181
DOI:
10.11992/tis.202204018
Abstract:
Label distribution learning (LDL) is a novel learning paradigm for solving labeling polysemy. Most existing LDL methods are designed based on complete data information; however, because of high labeling costs and the limitation of labelers’ level, complete labeling data information is difficult to obtain, which leads to performance degradation in traditional LDL algorithms. In this paper, we propose a novel weakly supervised LDL by maintaining a local label ranking (WSLDL-MLLR) algorithm. We improve algorithm performance under incomplete data labeling by maintaining relative relationships between the not-yet-missing labels and using label correlation to recover missing labels. Extensive experiments conducted on 14 datasets verified the effectiveness of the algorithm.
References:
[1] 耿新, 徐宁. 标记分布学习与标记增强[J]. 中国科学:信息科学, 2018, 48(5): 521–530
GENG Xin, XU Ning. Label distribution learning and label enhancement[J]. Scientia sinica:informationis, 2018, 48(5): 521–530
[2] 邵东恒, 杨文元, 赵红. 应用k-means算法实现标记分布学习[J]. 智能系统学报, 2017, 12(3): 325–332
SHAO Dongheng, YANG Wenyuan, ZHAO Hong. Label distribution learning based on k-means algorithm[J]. CAAI transactions on intelligent systems, 2017, 12(3): 325–332
[3] 王一宾, 李田力, 程玉胜. 结合谱聚类的标记分布学习[J]. 智能系统学报, 2019, 14(5): 966–973
WANG Yibin, LI Tianli, CHENG Yusheng. Label distribution learning based on spectral clustering[J]. CAAI transactions on intelligent systems, 2019, 14(5): 966–973
[4] 刘睿馨, 刘新媛, 李晨. 基于低秩表示的标记分布学习算法[J]. 模式识别与人工智能, 2021, 34(2): 146–156
LIU Ruixin, LIU Xinyuan, LI Chen. Label distribution learning method based on low-rank representation[J]. Pattern recognition and artificial intelligence, 2021, 34(2): 146–156
[5] MIAO Xu, ZHOU Zhihua. Incomplete label distribution learning[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track. Melbourne: IJCAI, 2017: 3175?3181.
[6] JIA Xiuyi, REN Tingting, CHEN Lei, et al. Weakly supervised label distribution learning based on transductive matrix completion with sample correlations[J]. Pattern recognition letters, 2019, 125: 453–462.
[7] ZENG Xueqiang, CHEN Sufen, XIANG Run, et al. Filling missing values by local reconstruction for incomplete label distribution learning[J]. International journal of wireless and mobile computing, 2019, 16(4): 314.
[8] ZENG Xueqiang, CHEN Sufen, XIANG Run, et al. Incomplete label distribution learning based on supervised neighborhood information[J]. International journal of machine learning and cybernetics, 2020, 11(1): 111–121.
[9] XU Chao, GU Shilin, TAO Hong, et al. Fragmentary label distribution learning via graph regularized maximum entropy criteria[J]. Pattern recognition letters, 2021, 145: 147–156.
[10] 任婷婷. 基于数据相关性挖掘的标记分布学习算法研究[D]. 南京: 南京理工大学, 2020.
REN Tingting. Label distribution learning algorithm based on based on data correlation mining[D]. Nanjing: Nanjing University of Science and Technology, 2020.
[11] 游美琳. 基于层次结构的标记分布学习研究[D]. 武汉: 中南财经政法大学, 2020.
YOU Meilin. Research on label distribution learning based on hierarchical structure[D]. Wuhan: Zhongnan University of Economics and Law, 2020.
[12] Abdi H. The method of least squares[J]. Encyclopedia of measurement and statistics, 2007, 1: 530–532.
[13] WE Deming. XI. The application of least squares[J]. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 1931, 11(68): 146–158.
[14] HUANG Jun, QIN Feng, ZHENG Xiao, et al. Improving multi-label classification with missing labels by learning label-specific features[J]. Information sciences, 2019, 492: 124–146.
[15] CHENG Yusheng, QIAN Kun, MIN Fan. Global and local attention-based multi-label learning with missing labels[J]. Information sciences, 2022, 594: 20–42.
[16] LI Xue, SHEN Bin, LIU Baodi, et al. Ranking-preserving low-rank factorization for image annotation with missing labels[J]. IEEE transactions on multimedia, 2018, 20(5): 1169–1178.
[17] JIA Xiuyi, SHEN Xiaoxia, LI Weiwei, et al. Label distribution learning by maintaining label ranking relation[J]. IEEE transactions on knowledge and data engineering, 2021(99): 1.
[18] LIU Dong C, NOCEDAL J. On the limited memory BFGS method for large scale optimization[J]. Mathematical programming, 1989, 45(1/2/3): 503–528.
[19] EISEN M B, SPELLMAN P T, BROWN P O, et al. Cluster analysis and display of genome-wide expression patterns[J]. Proceedings of the national academy of sciences of the United States of America, 1998, 95(25): 14863–14868.
[20] LYONS M, AKAMATSU S, KAMACHI M, et al. Coding facial expressions with Gabor wavelets[C]//Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. Nara: IEEE, 1998: 200?205.
[21] YIN Lijun, WEI Xiaozhou, SUN Yi, et al. A 3D facial expression database for facial behavior research[C]//7th International Conference on Automatic Face and Gesture Recognition. Southampton: IEEE, 2006: 211?216.
[22] PENG Kuanchuan, CHEN T, SADOVNIK A, et al. A mixed bag of emotions: model, predict, and transfer emotion distributions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 860?868.
[23] YANG Jufeng, SUN Ming, SUN Xiaoxiao. Learning visual sentiment distributions via augmented conditional probability neural network[C]//AAAI’17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. New York: ACM, 2017: 224?230.
[24] CHA S H. Comprehensive survey on distance/similarity measures between probability density functions[J]. International journal of mathematical models and methods in applied sciences, 2007, 1(4): 300–307.
[25] SPEARMAN C. The proof and measurement of association between two things[J]. International journal of epidemiology, 2010, 39(5): 1137–1150.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems