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 Package
Currently v1.2 [Adding AA-BP, AA-kNN, PT-Bayes and PT-SVM algorithm, updated 2016-5-1]
We have collected 15 real-world LDL data sets.Download LDL Data Sets
We have collected two multilabel ranking with inconsistent rankers datasets.Download Multilabel Ranking Data Sets
ChaLearn Looking at People Workshop, CVPR, 2016
2nd of Apparent Age EstimationMore Information
Xu N, Tao A, Geng X. Label Enhancement for Label Distribution Learning[C]//IJCAI. 2018: 2926-2932.Download Source Code
The 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:
 X. Geng. Label Distribution Learning. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2016, in press.
 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.
 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