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}
}

There is a demo in GMultiNMF/demo_digit.m working for hand-written digits recognition. You may see releases to access the full paper and download the demo dataset.

The accuracy (AC) and normalized mutual information (NMI) of different algorithms on three datasets:

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.