Abstract
Tensor component analysis, which can reveal the underlying structure of multiway data and exploit the relationship among multiple modes, plays an important role in signal processing and machine learning. However, with the development of sensors, we can obtain data that share common latent information but also maintain certain independent structures. For such data, traditional tensor-based methods process them in an independent manner, resulting in poor performance especially when some entries of data are missing.
To address this problem, coupled tensor component analysis is developed to process these data by modeling the modes with sharing latent information in a coupled way. In this chapter, we provide a detailed introduction for coupled tensor component analysis, including its development, popular algorithms, and applications. To be specific, we discuss coupled tensor decomposition from three ways according to different representations. They are coupled matrix and tensor factorization model, coupled tensor factorization model, and generalized coupled factorization model, respectively. Finally, we provide some applications including HSI-MSI fusion, link prediction, and visual data recovery for verification and comparison.
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Liu, Y., Liu, J., Long, Z., Zhu, C. (2022). Coupled Tensor for Data Analysis. In: Tensor Computation for Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-74386-4_5
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DOI: https://doi.org/10.1007/978-3-030-74386-4_5
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