论文标题
二元正交非负基质分解
Binary Orthogonal Non-negative Matrix Factorization
论文作者
论文摘要
我们提出了一种计算二进制正交非负矩阵分解（BONMF）进行聚类和分类的方法。该方法在几个代表性的现实世界数据集上进行了测试。数值结果证实，与相关技术相比，该方法的精度提高了。提出的方法快速用于训练，分类以及空间效率。
We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.
