@article{8c75e667-d1b2-4697-9e03-19ef81b14540,
author = {Andrzej Szwabe, Pawel Misiorek, Michal Ciesielczyk},
title = {Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices},
journal = {Schedae Informaticae},
volume = {2015},
number = {Volume 24},
year = {2016},
issn = {1732-3916},
pages = {103-112},keywords = {tensor-based data modeling; multilinear PCA; random indexing; dimensionality reduction; multilinear data filtering; higher-order SVD},
abstract = {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.},
doi = {10.4467/20838476SI.15.010.3032},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/multilinear-filtering-based-on-a-hierarchical-structure-of-covariance-matrices}
}