论文标题
布尔矩阵分解的最新发展
Recent Developments in Boolean Matrix Factorization
论文作者
论文摘要
布尔矩阵分解（BMF）的目的是将给定的二进制基质近似为两个低级别二元因子矩阵的乘积，在布尔代数下计算因子矩阵的乘积。尽管问题在计算上很难，但它也很有吸引力，因为因子矩阵的二元性质使它们高度可解释。在过去的十年中，BMF在数据挖掘和正式概念分析社区中受到了相当大的关注，最近，机器学习和理论社区也开始研究BMF。在这项调查中，我们简要介绍了所有这些社区的努力，并提出了一些公开问题，我们认为这些问题需要进一步调查。
The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as the product of two low-rank binary factor matrices, where the product of the factor matrices is computed under the Boolean algebra. While the problem is computationally hard, it is also attractive because the binary nature of the factor matrices makes them highly interpretable. In the last decade, BMF has received a considerable amount of attention in the data mining and formal concept analysis communities and, more recently, the machine learning and the theory communities also started studying BMF. In this survey, we give a concise summary of the efforts of all of these communities and raise some open questions which in our opinion require further investigation.
