Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices

• Autorzy: Szwabe A. , Misiorek P , Ciesielczyk M.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

tensor-based data modeling; multilinear PCA; random indexing; dimensionality reduction; multilinear data filtering; higher-order SVD

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 103--112
• Opis fizyczny: Bibliogr. 15 poz., tab.

Twórcy

autor

Szwabe A.

• Institute of Control and Information Engineering Poznań University of Technology ul. M. Skłodowskiej-Curie 5, 60-965 Poznań Poland

autor

Misiorek P

• Institute of Control and Information Engineering Poznań University of Technology ul. M. Skłodowskiej-Curie 5, 60-965 Poznań Poland

autor

Ciesielczyk M.

• Institute of Control and Information Engineering Poznań University of Technology ul. M. Skłodowskiej-Curie 5, 60-965 Poznań Poland

Bibliografia

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