IEEE Trans Pattern Anal Mach Intell - Sparse Canonical Correlation Analysis: New Formulation and Algorithm.

Tópicos

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Resumo

In this paper, we study canonical correlation analysis (CCA), which has become a powerful tool in multivariate data analysis for finding the correlations between two sets of multidimensional variables. The main contributions of the paper are: (i) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem; (ii) to obtain the explicit characterization of all solutions for the multiple CCA problem even the covariance matrices are singular; (iii) to develop a new sparse CCA algorithm; and (iv) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real world data sets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

Resumo Limpo

paper studi canon correl analysi cca becom power tool multivari data analysi find correl two set multidimension variabl main contribut paper reveal equival relationship recurs formula trace formula multipl cca problem ii obtain explicit character solut multipl cca problem even covari matric singular iii develop new spars cca algorithm iv establish equival relationship uncorrel linear discrimin analysi cca problem test sever simul real world data set gene classif crosslanguag document retriev demonstr effect propos algorithm perform propos method competit stateoftheart spars cca algorithm

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