Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

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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.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherIEEE
Pages1278-1283
Number of pages6
ISBN (Electronic)9781538691588
ISBN (Print)9781538691595
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Data Mining - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018

Conference

ConferenceIEEE International Conference on Data Mining
Abbreviated titleICDM
CountrySingapore
CitySingapore
Period17/11/201820/11/2018

Keywords

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

Cite this

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 (pp. 1278-1283). [8594981] IEEE. https://doi.org/10.1109/ICDM.2018.00172