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A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science Read chapter Read first chapter

2020 | OriginalPaper | Chapter

4. Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling

Authors : Robert E. Lowe, Michael W. Berry

Published in: Supervised and Unsupervised Learning for Data Science

Publisher: Springer International Publishing

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Abstract

Documents are seldom created in a vacuum. In all literature, there exists some influencing factor either in the form of cited documents, collaboration, or documents which authors have read. This influence can be seen within their works, and is present as a latent variable. This chapter demonstrates a novel method for quantifying these influences and representing them in a semantically understandable fashion. The model is constructed by representing documents as tensors, decomposing them into a set of factors, and then searching the corpus factors for similarity.

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Metadata
Title
Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling
Authors
Robert E. Lowe
Michael W. Berry
Copyright Year
2020
Book
Supervised and Unsupervised Learning for Data Science

Print ISBN: 978-3-030-22474-5

Electronic ISBN: 978-3-030-22475-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-22475-2_4