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A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets.

, , , , and . CoRR, (2020)

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Higher-Order Regularized Kernel CCA., and . ICMLA (1), page 374-377. IEEE, (2013)A Comparative Study of Kernel and Robust Canonical Correlation Analysis., , and . J. Multim., 5 (1): 3-11 (2010)IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations., , , and . J. Comput. Aided Mol. Des., 35 (3): 315-323 (2021)A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets., , , , and . CoRR, (2020)NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning., , , , , and . Briefings Bioinform., (2021)Hyperparameter Selection in Kernel Principal Component Analysis., and . J. Comput. Sci., 10 (7): 1139-1150 (2014)A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery., , and . CoRR, (2022)Robust Kernel Canonical Correlation Analysis to Detect Gene-Gene Interaction for Imaging Genetics Data., , , and . BCB, page 279-288. ACM, (2016)Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics., , and . Comput. Stat. Data Anal., (2018)A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development and osteoporosis., , , , and . J. Biomed. Informatics, (2021)