A variational autoencoder for probabilistic non-negative matrix factorisation.
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| Title: | A variational autoencoder for probabilistic non-negative matrix factorisation. |
|---|---|
| Authors: | Squires, Steven1 (AUTHOR) s.squires@exeter.ac.uk, Prügel Bennett, Adam1 (AUTHOR), Niranjan, Mahesan1 (AUTHOR) |
| Source: | Pattern Analysis & Applications. Dec2025, Vol. 28 Issue 4, p1-10. 10p. |
| Abstract: | We introduce and demonstrate the variational autoencoder (VAE) for probabilistic non-negative matrix factorisation (PAE-NMF). We design a network which can perform non-negative matrix factorisation (NMF) and add in aspects of a VAE to make the coefficients of the latent space probabilistic. By restricting the weights in the final layer of the network to be non-negative and using the non-negative Weibull distribution we produce a probabilistic form of NMF which allows us to generate new data and find a probability distribution that effectively links the latent and input variables. Our approach uses a minimum description length methodology to provide a method for achieving automatic regularisation; as it is designed using neural networks it can leverage deep learning frameworks for automatic differentiation, fast gradient descent algorithms and GPU support. We demonstrate the effectiveness of PAE-NMF on three heterogeneous datasets: images, financial time series and genomic. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Analysis & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A variational autoencoder for probabilistic non-negative matrix factorisation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Squires%2C+Steven%22">Squires, Steven</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.squires@exeter.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Prügel+Bennett%2C+Adam%22">Prügel Bennett, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Niranjan%2C+Mahesan%22">Niranjan, Mahesan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Analysis+%26+Applications%22">Pattern Analysis & Applications</searchLink>. Dec2025, Vol. 28 Issue 4, p1-10. 10p. – Name: Abstract Label: Abstract Group: Ab Data: We introduce and demonstrate the variational autoencoder (VAE) for probabilistic non-negative matrix factorisation (PAE-NMF). We design a network which can perform non-negative matrix factorisation (NMF) and add in aspects of a VAE to make the coefficients of the latent space probabilistic. By restricting the weights in the final layer of the network to be non-negative and using the non-negative Weibull distribution we produce a probabilistic form of NMF which allows us to generate new data and find a probability distribution that effectively links the latent and input variables. Our approach uses a minimum description length methodology to provide a method for achieving automatic regularisation; as it is designed using neural networks it can leverage deep learning frameworks for automatic differentiation, fast gradient descent algorithms and GPU support. We demonstrate the effectiveness of PAE-NMF on three heterogeneous datasets: images, financial time series and genomic. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Analysis & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10044-025-01563-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Titles: – TitleFull: A variational autoencoder for probabilistic non-negative matrix factorisation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Squires, Steven – PersonEntity: Name: NameFull: Prügel Bennett, Adam – PersonEntity: Name: NameFull: Niranjan, Mahesan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14337541 Numbering: – Type: volume Value: 28 – Type: issue Value: 4 Titles: – TitleFull: Pattern Analysis & Applications Type: main |
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