Randomized sketches for kernel CCA.
Neural Netw
; 127: 29-37, 2020 Jul.
Article
em En
| MEDLINE
| ID: mdl-32311655
ABSTRACT
Kernel canonical correlation analysis (KCCA) is a popular tool as a nonlinear extension of canonical correlation analysis. Consistency and optimal convergence rate have been established in the literature. However, the time complexity of KCCA scales as O(n3) and is thus prohibitive when n is large. We propose an m-dimensional randomized sketches approach for KCCA with m<Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Espacial
Tipo de estudo:
Clinical_trials
Limite:
Humans
Idioma:
En
Revista:
Neural Netw
Ano de publicação:
2020
Tipo de documento:
Article