We have implemented four LDL algorithms, namely IIS-LLD, BFGS-LLD, CPNN, LDSVR, aa. To help you start working with LDL, we provide three demos (See iisllddemo.m, bfgsllddemo.m, cpnndemo.m, ldsvrdemo.m) in this package. The readme.txt can help you start working with the package.
Download LDL Matlab PackageCurrently v1.2 [Adding AA-BP, AA-kNN, PT-Bayes and PT-SVM algorithm, updated 2016-5-1]
LDLPackage v1.1: contains IIS0LLD, BFGS-LLD, CPNN, LDSVR. Download
LDLPackage v1.0: contains IIS-LLD, BFGS-LLD, CPNN. Download
We have collected 14 real-world LDL data sets.
Download LDL Data SetsWe have collected two multilabel ranking with inconsistent rankers datasets.
Download Multilabel Ranking Data SetsChaLearn Looking at People Workshop, CVPR, 2016
2nd of Apparent Age Estimation
More InformationXu N, Tao A, Geng X. Label Enhancement for Label Distribution Learning[C]//IJCAI. 2018: 2926-2932.
Download Source CodeWang J, Geng X. Label distribution learning by exploiting label distribution manifold[J]//TNNLS. 2021
Download Source CodeZhao X, An, Y, Xu, N, Geng X. Continuous Label Distribution Learning[J]//PRJ. 2022.
Download Source CodeZhao X, An Y, Xu N, Wang J, Geng X. Imbalanced Label Distribution Learning[C]//AAAI. 2023.
Download Source CodeThe packages can be used freely for academic, non-profit purposes. If you intend to use it for commercial development, please contact us. In academic papers using this package, the following reference will be appreciated:
[1] X. Geng. Label Distribution Learning. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2016, 28(7): 1734-1748.
[2] X. Geng, C. Yin, and Z.-H. Zhou. Facial Age Estimation by Learning from Label Distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2013, 35(10): 2401-2412.
[3] Z.-W. H, X. Yang, X. Chao, et al. Deep Age Distribution Learning for Apparent Age Estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 17-24