Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices
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RIS BIB ENDNOTEMultilinear Filtering Based on a Hierarchical Structure of Covariance Matrices
Publication date: 11.04.2016
Schedae Informaticae, 2015, Volume 24, pp. 103-112
https://doi.org/10.4467/20838476SI.15.010.3032Authors
Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices
We propose a novel model of multilinear filtering based on a hierarchical structure of covariance matrices – each matrix being extracted from the input tensor in accordance to a specific set-theoretic model of data generalization, such as derivation of expectation values. The experimental analysis results presented in this paper confirm that the investigated approaches to tensor-based data representation and processing outperform the standard collaborative filtering approach in the ‘cold-start’ personalized recommendation scenario (of very sparse input data). Furthermore, it has been shown that the proposed method is superior to standard tensor-based frameworks such as N-way Random Indexing (NRI) and Higher-Order Singular Value Decomposition (HOSVD) in terms of both the AUROC measure and computation time.
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Information: Schedae Informaticae, 2015, Volume 24, pp. 103-112
Article type: Original article
Institute of Control and Information Engineering Poznan University of Technology
Institute of Control and Information Engineering Poznan University of Technology
Institute of Control and Information Engineering Poznan University of Technology
Published at: 11.04.2016
Article status: Open
Licence: None
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