Joint Linked Component Analysis for Multiview Data
- URL: http://arxiv.org/abs/2406.11761v1
- Date: Mon, 17 Jun 2024 17:25:23 GMT
- Title: Joint Linked Component Analysis for Multiview Data
- Authors: Lin Xiao, Luo Xiao,
- Abstract summary: We formulate a matrix decomposition model where a joint structure and an individual structure are present in each data view.
An objective function with a novel penalty term is then proposed to achieve simultaneous estimation and rank selection.
- Score: 6.588932144201398
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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