Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Indian Institute of Technology


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
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


ConferenceIEEE International Conference on Data Mining
Abbreviated titleICDM

    Research areas

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

Download statistics

No data available

ID: 31073529