**5. Conclusions**

In this paper, we provide a novel nonnegative matrix factorization with data-guided constraint (DGC-NMF), which is based on the data's sparsity levels in different areas. Since the sparseness of abundances is previously unknown, we provide a method to evaluate the sparsity level of each pixel's abundances. The sparseness map of data is estimated by using the obtained abundances in a NMF unmixing process with no constraint. The experiments results validate that the estimated sparseness values can represent the real sparsity levels of pixels well. Through the estimated sparseness map, sparseness constraints on pixels' abundances could be adaptively imposed and lead to better unmixing results. We have proven monotone decrease of the objective by our algorithm and illustrated the effectiveness and practicability of the algorithm by experiments on synthetic data and real hyperspectral images. For the future work, the performance of our method could be further improved by achieving a more accurate estimation of sparsity levels and by introducing more reasonable constraints imposing strategy. More methods based on mining and using the information latent in data itself would also be worthy of further study.

**Acknowledgments:** This work was supported by National Nature Science Foundation of China (No. 61571170, No. 61671408 ) and the Joint Funds of the Ministry of Education of China (No. 6141A02022314).

**Author Contributions:** All the authors made significant contributions to the work. Risheng Huang designed the research and analyzed the results. Xiaorun Li provided advice for the preparation and revision of the paper. Liaoying Zhao assisted in the preparation work and validation work.

**Conflicts of Interest:** The authors declare no conflict of interest.
