Browsing by Subject "sparse canonical correlation analysis"

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    Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease
    (IEEE, 2022-12) Sha, Jiahang; Bao, Jingxuan; Liu, Kefei; Yang, Shu; Wen, Zixuan; Cui, Yuhan; Wen, Junhao; Davatzikos, Christos; Moore, Jason H.; Saykin, Andrew J.; Long, Qi; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
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    Sparse Canonical Correlation Analysis via Truncated ℓ1-norm with Application to Brain Imaging Genetics
    (IEEE, 2016-12) Du, Lei; Zhang, Tuo; Liu, Kefei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Guo, Lei; Saykin, Andrew J.; Shen, Li; Medical and Molecular Genetics, School of Medicine
    Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants. The ℓ0-norm is more desirable, which however remains unexplored since the ℓ0-norm minimization is NP-hard. In this paper, we impose the truncated ℓ1-norm to improve the performance of the ℓ1-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.