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MATLAB toolbox for the multi-label algorithm ML^2

Copyright
Peng Hou (hpeng@seu.edu.cn), Xin Geng (xgeng@seu.edu.cn), Min-Ling Zhang (zhangml@seu.edu.cn)
School of Computer Science and Engineering, Southeast University
Nanjing 211189, P.R.China
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0. Contents
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0. Contents
1. Introduction
2. Requirements
3. Installation
4. How to start?

1.Introduction
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This package implements a novel multi-label algorithm named ML^2 (Multi-Label Manifold Learning).
The algorithm ML^2 includes three main steps. First, it estimates the topological strucutre 
from the feature space. Then it transfers the topological structure to the label space and 
constructs the label manifold. Finally, it adapts MSVR to learn the mapping from the feature 
manifold to the label manifold. Further details about ML^2 can be found in the following papers:

P. Hou, X. Geng, M.-L. Zhang. Multi-label manifold learning. In: Proceedings of the 30th AAAI 
Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, in press.

This package can be used freely for academic, non-profit purposes. 
If you intend to use it for commercial development, please contact us. 
Please feel free to contact us if you find anything wrong 
or you have any further questions.

2. Requirements
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- Matlab, version 2014a and higher.
- The package is mostly self-contain. 
Several functions require the Optimization Toolbox. 

3. Installation
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- Create a directory of your choice and copy the toolbox there.
- Set the path in your Matlab to add the directory you just created.

4. How to start?
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To help you start working with LDL, we provide a demo(See demo.m) in this package. 
You can follow the procedure as described in the demo.

Please read and play with the demo to get started. Have fun!
