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Multiview and multifeature spectral clustering using common eigenvectors

Autor
Kanaan-Izquierdo, S.; Ziyatdinov, A.; Perera, A.
Tipus d'activitat
Article en revista
Revista
Pattern recognition letters
Data de publicació
2018-01-15
Volum
102
Pàgina inicial
30
Pàgina final
36
DOI
https://doi.org/10.1016/j.patrec.2017.12.011 Obrir en finestra nova
Projecte finançador
Impacto del entrenamiento en deportistas de élite en la función cardíaca, regulación neural y regulación genética asociada
Serious Games on Heart Failure patients. Estimation of their benefits on the Spanish Health System
Repositori
http://hdl.handle.net/2117/113182 Obrir en finestra nova
https://www.sciencedirect.com/science/article/pii/S016786551730449X Obrir en finestra nova
Resum
An ever-increasing number of data analysis problems include more than one view of the data, i.e. differ- ent measurement approaches to the population under study. In consequence, pattern analysis methods that deal appropriately with multiview data are becoming increasingly useful. In this paper, a novel mul- tiview spectral clustering algorithm is presented (multiview spectral clustering by common eigenvectors, or MVSC-CEV), based on computing the common eigenvectors of the Laplacian matrices de...
Citació
Kanaan-Izquierdo, S., Ziyatdinov, A., Perera, A. Multiview and multifeature spectral clustering using common eigenvectors. "Pattern recognition letters", 15 Gener 2018, vol. 102, p. 30-36.
Paraules clau
Multiview data Spectral clustering Common eigenvectors
Grup de recerca
B2SLab - Bioinformatics and Biomedical Signals Laboratory
CREB - Centre de Recerca en Enginyeria Biomedica
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla

Participants