Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Authors
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
2018 IEEE International Conference on Data Mining, ICDM 2018, pp. 1278-1283
Abstract
We present SCCA-HSIC, a method for finding sparse non-linear multivariate relations in high-dimensional settings by maximizing the Hilbert-Schmidt Independence Criterion (HSIC). We propose efficient optimization algorithms using a projected stochastic gradient and Nyström approximation of HSIC. We demonstrate the favourable performance of SCCA-HSIC over competing methods in detecting multivariate non-linear relations both in simulation studies, with varying numbers of related variables, noise variables, and samples, as well as in real datasets.Description
Other note
Citation
Uurtio, V, Bhadra, S & Rousu, J 2018, Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion. in 2018 IEEE International Conference on Data Mining, ICDM 2018., 8594981, IEEE, pp. 1278-1283, IEEE International Conference on Data Mining, Singapore, Singapore, 17/11/2018. https://doi.org/10.1109/ICDM.2018.00172