ACM Transactions on Knowledge Discovery from Data· 2019Q1· Review
Tensor Completion Algorithms in Big Data Analytics
- 220citations
- Q1SCImago
- 2019year
Short summary
This survey provides a modern overview of tensor completion algorithms, focusing on their application to big data analytics characterized by variety, volume, and velocity.
AI-generated from the title and abstract; the full text is not read.
Key points
- Tensor completion addresses filling missing entries in partially observed tensors, crucial for complex datasets.
- Recent advances are analyzed through the lens of big data characteristics: variety, volume, and velocity.
- The survey covers general algorithms, methods using auxiliary information, scalable techniques, and dynamic tensor completion.
- It includes real-world applications, common experimental setups, and available software for practitioners.
AI-generated from the title and abstract; the full text is not read.
Abstract
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from the following four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature along with several available software repositories. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.
The authors' abstract, as published at the source. ACM Transactions on Knowledge Discovery from Data, 2019 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Computational Mathematics
Computational MathematicsMathematics