%0 Journal Article
%T Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices
%A Szwabe, Andrzej
%A Misiorek, Pawel
%A Ciesielczyk, Michal
%J Schedae Informaticae
%V 2015
%R 10.4467/20838476SI.15.010.3032
%N Volume 24
%P 103-112
%K tensor-based data modeling, multilinear PCA, random indexing, dimensionality reduction, multilinear data filtering, higher-order SVD
%@ 1732-3916
%D 2016
%U https://ejournals.eu/en/journal/schedae-informaticae/article/multilinear-filtering-based-on-a-hierarchical-structure-of-covariance-matrices
%X 
	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.