LDL Package

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]


LDLPackage v1.1: contains IIS0LLD, BFGS-LLD, CPNN, LDSVR. Download
LDLPackage v1.0: contains IIS-LLD, BFGS-LLD, CPNN. Download

Data Sets

We have collected 15 real-world LDL data sets.

Download LDL Data Sets

Multilabel Ranking with Inconsistent Rankers - Datasets

We have collected two multilabel ranking with inconsistent rankers datasets.

Download Multilabel Ranking Data Sets

Deep Age Distribution Learning for Apparent Age Estimation

ChaLearn Looking at People Workshop, CVPR, 2016

2nd of Apparent Age Estimation

More Information

Label enhancement for label distribution learning

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:

[1] X. Geng. Label Distribution Learning. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2016, in press.

[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