Abstract:Many modern datasets consist of multiple related matrices measured on a common set of units, with the goal of recovering a shared low-dimensional subspace. The Angle-based Joint and Individual Variation Explained (AJIVE) framework addresses this problem through equal-weight aggregation, which can be suboptimal when views exhibit statistical heterogeneity in signal-to-noise ratios and dimensions, as well as structural heterogeneity from individual components. For equal-weight AJIVE, we show that the previously identified ``non-diminishing'' error barrier is geometry dependent: under near-orthogonal deterministic loading orientations, the second-order term is reduced, whereas under sign-symmetric random loadings, it is centered and averages out, yielding a $K^{-1/2}$-type rate without iterative refinement. Under a majority sign-alignment condition in rank-one setting, a bias at the squared single-view perturbation scale can persist. For general weights, we establish error bounds that disentangle the two layers of heterogeneity, and propose HeteroJIVE, the weighted AJIVE estimator using an explicit weight that is optimal whenever its identifiability gap is constant. We also provide a data-driven plug-in implementation of HeteroJIVE, together with an optional geometry-adaptive extension of this data-driven procedure. Simulations and analyses of multi-omics and image data illustrate the practical benefits of HeteroJIVE.
From: Zhongyuan Lyu [view email]
[v1]
Tue, 2 Dec 2025 15:28:07 UTC (2,287 KB)
[v2]
Fri, 31 Jul 2026 09:41:16 UTC (1,022 KB)
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