Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

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openAccess

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Journal Title

Journal ISSN

Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2018

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Mcode

Degree programme

Language

en

Pages

6
1278-1283

Series

2018 IEEE International Conference on Data Mining, ICDM 2018

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.

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Keywords

Canonical correlation, Dimensionality reduction, Hilbert-schmidt independence criterion, Kernel methods, Sparsity

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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