Tensor Completion Algorithms in Big Data Analytics

TL;DR

This survey reviews recent advances in tensor completion algorithms tailored for big data analytics, emphasizing methods that handle diverse data types, large datasets, and dynamic environments across multiple application domains.

Contribution

It provides a comprehensive overview of recent tensor completion techniques considering variety, volume, and velocity, and discusses their applications and future research directions.

Findings

Summarizes recent tensor completion algorithms for big data.

Highlights applications in real-world data-driven problems.

Identifies key challenges and future research directions.

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 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. 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.