Tensor Regression

Liu, Yipeng; Liu, Jiani; Zhang, Long; Zhu, Ce

doi:10.1007/978-3-030-74386-4_7

Tensor Computation for Data Analysis 2021

DOI: 10.1007/978-3-030-74386-4_7

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Tensor Regression

Yipeng Liu

Jiani Liu

Long Zhang

et al.

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2022

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Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches

Zdunek

Fonał

2022

Symmetry

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Nonnegative Tucker decomposition (NTD) is a robust method used for nonnegative multilinear feature extraction from nonnegative multi-way arrays. The standard version of NTD assumes that all of the observed data are accessible for batch processing. However, the data in many real-world applications are not static or are represented by a large number of multi-way samples that cannot be processing in one batch. To tackle this problem, a dynamic approach to NTD can be explored. In this study, we extend the standard model of NTD to an incremental or online version, assuming volatility of observed multi-way data along one mode. We propose two computational approaches for updating the factors in the incremental model: one is based on the recursive update model, and the other uses the concept of the block Kaczmarz method that belongs to coordinate descent methods. The experimental results performed on various datasets and streaming data demonstrate high efficiently of both algorithmic approaches, with respect to the baseline NTD methods.

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Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches

Zdunek

Fonał

2022

Symmetry

View full text Add to dashboard Cite

show abstract

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Tensor Regression

Cited by 1 publication

References 36 publications

Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches

Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches

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