Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

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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