期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 35, 期 5, 页码 5132-5146出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2022.3145489
关键词
Matrix decomposition; Computational modeling; Data models; Convergence; Optimization; Scalability; Partitioning algorithms; Non-negative matrix tri-factorization; parallel computing; message passing; Newton iteration
Non-negative Matrix Tri-Factorization (NMTF) is a novel paradigm for data mining and dimensionality reduction that has gained attention due to its notable performance and elegant mathematical derivation. However, existing NMTF-based methods suffer from high computational complexity. This paper proposes a parallel and scalable NMTF-based algorithm for text data co-clustering.
As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real-world applications, such as text data co-clustering. However, the existing NMTF-based methods usually involve intensive matrix multiplications, which exhibits a major limitation of high computational complexity. With the explosion at both the size and the feature dimension of texts, there is a growing need to develop a parallel and scalable NMTF-based algorithm for text data co-clustering. To this end, we first show in this paper how to theoretically derive the original optimization problem of NMTF by introducing the Lagrangian multipliers. Then, we propose to solve the Lagrange dual objective function in parallel through an efficient distributed implementation. Extensive experiments on five benchmark corpora validate the effectiveness, efficiency, and scalability of our distributed parallel update algorithm for an NMTF-based text data co-clustering method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据