- Tytuł:
- Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices
- Autorzy:
-
Szwabe, Andrzej
Ciesielczyk, Michal
Misiorek, Pawel - Powiązania:
- https://bibliotekanauki.pl/articles/1373696.pdf
- Data publikacji:
- 2015
- Wydawca:
- Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
- Tematy:
-
tensor-based data modeling
multilinear PCA
random indexing
dimensionality reduction
multilinear data filtering
higher-order SVD - Opis:
- 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.
- Źródło:
-
Schedae Informaticae; 2015, 24; 103-112
0860-0295
2083-8476 - Pojawia się w:
- Schedae Informaticae
- Dostawca treści:
- Biblioteka Nauki
Artykuł