Large-Scale Sparse Kernel Canonical Correlation Analysis

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Indian Institute of Technology Palakkad

Kuvaus

This paper presents gradKCCA, a large-scale sparse non-linear canonical correlation method. Like Kernel Canonical Correlation Analysis (KCCA), our method finds non-linear relations through kernel functions, but it does not rely on a kernel matrix, a known bottleneck for scaling up kernel methods. gradKCCA corresponds to solving KCCA with the additional constraint that the canonical projection directions in the kernel-induced feature space have preimages in the original data space. Firstly, this modification allows us to very efficiently maximize kernel canonical correlation through an alternating projected gradient algorithm working in the original data space. Secondly, we can control the sparsity of the projection directions by constraining the ℓ1 norm of the preimages of the projection directions, facilitating the interpretation of the discovered patterns, which is not available through KCCA. Our empirical experiments demonstrate that gradKCCA outperforms state-of-the-art CCA methods in terms of speed and robustness to noise both in simulated and real-world datasets.

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko36th International Conference on Machine Learning, ICML 2019
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Machine Learning - Long Beach, Yhdysvallat
Kesto: 9 kesäkuuta 201915 kesäkuuta 2019
Konferenssinumero: 36

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaPMLR
Vuosikerta97
ISSN (elektroninen)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
LyhennettäICML
MaaYhdysvallat
KaupunkiLong Beach
Ajanjakso09/06/201915/06/2019

ID: 36784375