Update README.md.

Signed-off-by: CyberZHG <[email protected]>

README.md

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 # Graph-Multi-NMF-Feature-Clustering
-Codes for __Feature Extraction via Multi-view Non-negative Matrix Factorization with Local Graph Regularization__
+
+## Introduction
+
+Codes for __Feature Extraction via Multi-view Non-negative Matrix Factorization with Local Graph Regularization__. 
+
+Motivated by manifold learning and multi-view Non-negative Matrix Factorization (NMF), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem.
+
+Please cite the following information:
+
+```
+@inproceedings{wang2015multi,
+ title={Feature Extraction via Multi-view Non-negative Matrix Factorization with Local Graph Regularization},
+ author={Wang, Zhenfan and Kong, Xiangwei and Fu, Haiyan and Li, Ming and Zhang, Yujia},
+ booktitle={Image Processing (ICIP), 2015 IEEE International Conference on},
+ year={2015},
+ organization={IEEE}
+}
+```
+
+## Demo
+
+There is a demo in `GMultiNMF/demo_digit.m` working for hand-written digits recognition. See releases to download the full demo with dataset.
+
+## Results
+
+The accuracy (AC) and normalized mutual information (NMI) of different algorithms on three datasets:
+
+![1](https://cloud.githubusercontent.com/assets/853842/8086601/c6d273f2-0fc8-11e5-8ceb-85c84239ec06.png)
+
+![2](https://cloud.githubusercontent.com/assets/853842/8086602/c70111a8-0fc8-11e5-9f6e-63f4d02a67b7.png)
+
+From the tables, we can see that our proposed algorithm performs better in each dataset in terms of AC and NMI. Although other methods consider multiple feature integration, Co-reguSC and SC-ML use latent data relationship, the results demonstrate that our proposed Multi-view NMF with local graph regularization feature extraction framework can learn a better feature representation.