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 scientific article
Published at: 11.04.2016
Article status: Open
Licence: None
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