Loading...
Loading...

Go to the content (press return)

Multiview and multifeature spectral clustering using common eigenvectors

Author
Kanaan-Izquierdo, S.; Ziyatdinov, A.; Perera, A.
Type of activity
Journal article
Journal
Pattern recognition letters
Date of publication
2018-01-15
Volume
102
First page
30
Last page
36
DOI
https://doi.org/10.1016/j.patrec.2017.12.011 Open in new window
Project funding
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
Repository
http://hdl.handle.net/2117/113182 Open in new window
https://www.sciencedirect.com/science/article/pii/S016786551730449X Open in new window
Abstract
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...
Citation
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.
Keywords
Multiview data Spectral clustering Common eigenvectors
Group of research
B2SLab - Bioinformatics and Biomedical Signals Laboratory
CREB - Biomedical Engineering Research Centre
TALP - Centre for Language and Speech Technologies and Applications

Participants