Joint Linked Component Analysis for Multiview Data
<|reference_start|>Joint Linked Component Analysis for Multiview Data: In this work, we propose the joint linked component analysis (joint\_LCA) for multiview data. Unlike classic methods which extract the shared components in a sequential manner, the objective of joint\_LCA is to identify the view-specific loading matrices and the rank of the common latent subspace simultaneously. We formulate a matrix decomposition model where a joint structure and an individual structure are present in each data view, which enables us to arrive at a clean svd representation for the cross covariance between any pair of data views. An objective function with a novel penalty term is then proposed to achieve simultaneous estimation and rank selection. In addition, a refitting procedure is employed as a remedy to reduce the shrinkage bias caused by the penalization.<|reference_end|>
@article{xiao2024joint,
    title={Joint Linked Component Analysis for Multiview Data},
    author={Lin Xiao, Luo Xiao},
    journal={arXiv preprint arXiv:2406.11761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11761},
    primaryClass={stat.ML cs.LG stat.ME}
}