During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results w.r.t. standard measures in BMF is missing. In this paper, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms—from different standpoints—existing state-of-the-art BMF algorithms.

在过去的几年中，布尔矩阵分解（BMF）已成为数据分析的重要方向。最小描述长度原则（MDL）已成功地在BMF中适用于模型顺序选择。但是，缺少使用BMF中的标准度量执行良好结果的BMF算法。在本文中，我们提出了一种基于形式概念分析的从下面开始的布尔矩阵分解算法。该算法利用MDL原理作为因素选择的标准。在各种实验中，我们表明，从不同的角度来看，所提出的算法要优于现有的最新BMF算法。