Applying non-negative tensor factorization to centered data (notice n° 99726)

détails MARC
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fixed length control field 02232cam a2200349 4500500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250112004332.0
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Language code of text/sound track or separate title fre
042 ## - AUTHENTICATION CODE
Authentication code dc
100 10 - MAIN ENTRY--PERSONAL NAME
Personal name Fogel, Paul
Relator term author
245 00 - TITLE STATEMENT
Title Applying non-negative tensor factorization to centered data
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Date of publication, distribution, etc. 2023.<br/>
500 ## - GENERAL NOTE
General note 44
520 ## - SUMMARY, ETC.
Summary, etc. We present here an original application of the non-negative matrix factorization (NMF) method, applied to the case of extra-financial data. NMF allows to reduce the useful dimension of a dataset by simultaneously creating new meta-features linked to the original variables through non-negative loadings, and nonnegative scores linking the observations to the meta-features. Thanks to the non-negativity constraints, meta-features can be easily interpreted by looking at the features with the highest loadings in the NMF representation. However, the lowest loadings are generally ignored. We show that this asymmetrical treatment can be problematic in some instances of data sets. The innovation introduced in this paper is to apply a tensorized version of NMF to centered data, which we call Semi Non-Negative Tensor Factorization (semi-NTF). The method is illustrated on a set of ESG scores of European equity issuers, resulting in a fully interpretable reduced set of meta-features. In particular, we show that the scores associated with these meta-features are significantly less correlated with each other than the ready-to-use ESG scores, leading to improved discriminatory power of the meta-features. JEL Classification: C02, C14, C65, C81.
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Topical term or geographic name as entry element PCA
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Topical term or geographic name as entry element semi-NTF
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Topical term or geographic name as entry element Principal Components
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Topical term or geographic name as entry element ESG data
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Topical term or geographic name as entry element Interpretability
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Topical term or geographic name as entry element Factor Analysis
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Topical term or geographic name as entry element Semi-NMF
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Topical term or geographic name as entry element PosNegNMF
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Topical term or geographic name as entry element NTF
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Topical term or geographic name as entry element Classification Methods
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Topical term or geographic name as entry element Cluster Analysis
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Topical term or geographic name as entry element Dimension Reduction
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Topical term or geographic name as entry element NMF
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Geissler, Christophe
Relator term author
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Von Mettenheim, Hans-Jörg
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700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Luta, George
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786 0# - DATA SOURCE ENTRY
Note Bankers, Markets & Investors | 174 | 3 | 2023-11-02 | p. 2-13
856 41 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://shs.cairn.info/revue-bankers-markets-et-investors-2023-3-page-2?lang=en">https://shs.cairn.info/revue-bankers-markets-et-investors-2023-3-page-2?lang=en</a>

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