期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 8, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2915921
关键词
Tensors; tensor decomposition; tensor factorization; multi-aspect data; multi-way analysis
资金
- National Science Foundation [IIS-1247489, IIS-1247632]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1247489, 1247632] Funding Source: National Science Foundation
Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner's point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today's big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.
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