Computer Science ›› 2020, Vol. 47 ›› Issue (8): 132-136.doi: 10.11896/jsjkx.190700012

Special Issue: Big Data & Data Scinece

Previous Articles     Next Articles

Label Distribution Learning Based on Natural Neighbors

YAO Cheng-liang, ZHU Qing-sheng   

  1. Chongqing Key Lab of Software Theory and Technology, College of Computer Science, Chongqing University, Chongqing 400044, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:YAO Cheng-liang, born in 1990, postgraduate.His main research interests include data mining and machine lear-ning.
    ZHU Qing-sheng, born in 1956, professor, Ph.D supervisor, is a member of China Computer Federation.His main research interests include service-oriented software engineering, data mining and outlier detection, and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61802360) and Science and Technology Project of Chongqing (KJZH7104).

Abstract: Label distribution is a new machine learning paradigm which can solve some markup ambiguity problems well.Label distribution can be seen as the generalization of multi-label.Traditional single-label learning and multi-label learning can be seen as special cases of label distribution learning.AA-KNN based on algorithm adaptive is an effective algorithm, but it is diffcult to choose an appropriate parameter K which affects the perfomence when KNN is used.So, Natural neighbors is introduced into LDL and a new label distribution learning algorithm is proposed.It finds natural neighbors of each object by searching algorithm, and then gets the average of labels of these neighbours as the predicted result.The natural neighbours searching algorithm does not need any parameter and is passive so that neighbors of each object is decided automatically.Experiments was conducted on 6 data sets and 6 evaluation indexes.The experiments show that the proposed algorithm not only solves the problem of choosing parameter K, but also improves the performance compared with AA-KNN.

Key words: Label distribution, Label distribution learning, Natural neighbors, No-parameter

CLC Number: 

  • TP391
[1]TSOUMAKAS G, KATAKIS I.Multi-label classification:an overview[J].International Journal of Data Warehousing and Mi-ning, 2007, 3(3):1-13.
[2]GENG X, LUO L.Multilabel ranking with inconsistent rankers[C]∥Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.Columbus, 2014:3742-3747.
[3]GENG X.Label distribution learning[J].IEEE Trans KnowlData Eng, 2016, 28:1734-1748.
[4]GENG X, XU N.Label distribution learning and label enhancement[J].Sci Sin Inform, 2018, 48(5):521-530.
[5]ZHANG Z, WANG M, GENG X.Crowd counting in public video surveillance by label distribution learning[J].Neurocomputing, 2015, 166(C):151-163.
[6]GENG X, XIA Y.Head pose estimation based on multivariate label distribution[C]∥Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014:1837-1842.
[7]GENG X, HOU P.Pre-relesse prediction of crowd opinion on movies by label distribution learning[C]∥Proceedings of the International Joint Conference on Artificial Intelligence.Bueons Aires, Argentina, San Francisco, USA:Morgan Kaufmann, 2015:3511-3517.
[8]GENG X, YIN C, ZHOU Z H.Facial age estimation by learning from label distribution[J].IEEE Trasaction on Pattern Analysis and Machine Intelligence, 2013, 35(10):2401-2412.
[9]LING M, GENG X.Indoor Crowd Counting by Mixture ofGaussians Label Distribution Learning[J].IEEE Transactions on Image Processing, 2019, 28(11):5691-5701.
[10]XU L, CHEN J, GAN Y.Head pose estimation using improved label distribution learning with fewer annotations. Multimedia Tools and Applications, 2019, 78(14):19141-19162.
[11]HUANG J, ZHU Q, YANG L, et al.A non-parameter outlier detection algorithm based on Natural Neighbor[J].Knowledge-Based Systems, 2016, 92:71-77.
[12]YANG L, ZHU Q, HUANG J, et al.Adaptive edited naturalneighbor algorithm[J].Neurocomputing, 2017, 230:427-433.
[13]ZHAO Q, GENG X.Selection of target function in label distribution learning[J].Journal of Frontiers of Computer Science and Technology, 2017, 11(5):708-719.
[14]ZHU Q, FENG J, HUANG J.Natural neighbor:A self-adaptive neighborhood method without parameter K[J].Pattern Recognition Letters, 2016, 80(1):30-36.
[15]LIU R, WANG H, YU X.Shared-nearest-neighbor-based clustering by fast search and find of density peaks[J].Information Sciences, 2018, 450:200-226.
[16]ZHU Q, FENG J, HUANG J.Natural neighbor:A self-adaptive neighborhood method without parameter K[J].Pattern Recognition Letters, 2016, 80:30-36.
[17]ZHANG M L, ZHOU Z H.A Review on Multi-Label Learning Algorithms[J].IEEE Transactions on Knowledge & Data Engineering, 2014, 26(8):1819-1837.
[18]CHEN M, LI L J, WANG B, et al.Effectively clustering by finding density backbone based-on kNN[J].Pattern Recognition, 2016, 60:486-4988.
[1] DONG Xin-yue, FAN Rui-dong, HOU Chen-ping. Active Label Distribution Learning Based on Marginal Probability Distribution Matching [J]. Computer Science, 2020, 47(9): 190-197.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!