Results 11 to 20 of about 15,041 (261)
Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics [PDF]
Medical Image Analysis, 2022 The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data.
Mansu Kim, Eun Jeong Min, Kefei Liu
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Robust sparse canonical correlation analysis. [PDF]
BMC Syst Biol, 2016 Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation. This paper discusses a method for Robust Sparse CCA.
Wilms I, Croux C.
europepmc +7 more sources
On Sparse Canonical Correlation Analysis
Advances in Neural Information Processing Systems 37, 2023 The classical Canonical Correlation Analysis (CCA) identifies the correlations between two sets of multivariate variables based on their covariance, which has been widely applied in diverse fields such as computer vision, natural language processing, and speech analysis.
Yongchun Li +2 more
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IMAGING GENETICS VIA SPARSE CANONICAL CORRELATION ANALYSIS. [PDF]
Proc IEEE Int Symp Biomed Imaging, 2013 The collection of brain images from populations of subjects who have been genotyped with genome-wide scans makes it feasible to search for genetic effects on the brain. Even so, multivariate methods are sorely needed that can search both images and the genome for relationships, making use of the correlation structure of both datasets.
Chi EC +5 more
europepmc +6 more sources
Sparse canonical correlation analysis from a predictive point of view [PDF]
Biometrical Journal, 2013 Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each dataset. However, in high‐dimensional settings where the number of variables exceeds the sample size or when the variables are highly correlated, traditional CCA is no longer ...
Wilms, Ines, Croux, Christophe
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Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data [PDF]
Statistical Applications in Genetics and Molecular Biology, 2009 In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other.
Daniela M Witten
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A Mathematical Programming Approach to Sparse Canonical Correlation Analysis [PDF]
Expert Systems with Applications, 2023 Recent developments in the interplay between Operational Research and Statistics allowed us to exploit advances in Mixed-Integer Optimisation (MIO) solvers to improve the quality of statistical analysis. In this work, we tackle Canonical Correlation Analysis (CCA), a dimensionality reduction method that jointly summarises multiple data sources while ...
Lavinia Amorosi +3 more
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Robust and sparse canonical correlation analysis based L(2,p)-norm
The Journal of Engineering, 2017 The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features.
Zhong-rong Shi +3 more
doaj +2 more sources
Structured Sparse Canonical Correlation Analysis
, 2018 In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes.
Xi Chen 0010 +2 more
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Journal of Multivariate Analysis, 2021 Classical canonical correlation analysis (CCA) requires matrices to be low dimensional, i.e. the number of features cannot exceed the sample size. Recent developments in CCA have mainly focused on the high-dimensional setting, where the number of features in both matrices under analysis greatly exceeds the sample size. These approaches impose penalties
Wenjia Wang, Yi-Hui Zhou
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