Boolean Matrix Decomposition Problem: Theory, Variations and Applications to Data Engineering
Boolean Matrix Decomposition Problem: Theory, Variations and Applications to Data Engineering
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布尔矩阵分解问题：理论、变体及其在数据工程中的应用
DOI：
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发表时间：
2012
期刊：
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通讯作者：
Jaideep Vaidya
中科院分区：
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作者：
Jaideep Vaidya
由于数据收集无处不在且规模庞大，数据汇总问题对于有效的数据管理至关重要。经典的矩阵分解技术经常用于此目的，并且已成为许多研究的主题。近年来，包括布尔矩阵分解在内的几种其他形式的分解已变得具有重大的实际意义。由于收集的大部分数据本质上是分类的，因此可以用布尔矩阵来查看。布尔矩阵分解（BMD），其中布尔矩阵被表示为两个布尔矩阵的乘积，可用于提供布尔数据集的简洁且可解释的表示。分解的矩阵给出了一组有意义的概念及其组合，可用于重建原始数据。这种分解在许多应用领域都很有用，包括角色工程、文本挖掘以及数据库知识发现。在本次研讨会中，我们将探讨 BMD 问题的理论基础，研究其一些变体和解决方案，并研究不同的实际应用。
With the ubiquitous nature and sheer scale of data collection, the problem of data summarization is most critical for effective data management. Classical matrix decomposition techniques have often been used for this purpose, and have been the subject of much study. In recent years, several other forms of decomposition, including Boolean Matrix Decomposition have become of significant practical interest. Since much of the data collected is categorical in nature, it can be viewed in terms of a Boolean matrix. Boolean matrix decomposition (BMD), wherein a boolean matrix is expressed as a product of two Boolean matrices, can be used to provide concise and interpretable representations of Boolean data sets. The decomposed matrices give the set of meaningful concepts and their combination which can be used to reconstruct the original data. Such decompositions are useful in a number of application domains including role engineering, text mining as well as knowledge discovery from databases. In this seminar, we look at the theory underlying the BMD problem, study some of its variants and solutions, and examine different practical applications.