arXiv (All)
open access
Testing and segmentation of joint and individual components in integrative multi-source factor models
Disentangling shared (joint) structures from source-specific (individual) variations is a fundamental task in multi-source data integration. Existing joint-individual models often rely on computationally intensive optimization or loose spectral bounds, leading to suboptimal separation accuracy and p
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Distributionally Robust Federated Learning with Multi-Source Data#1034317
- Distributionally Robust Federated Learning with Multi-Source Data#978438
- An efficient EM algorithm for both element-wise and structural missingness in matrix-variate normal mixture models#820041
- A Two Stage Quasi-Likelihood Estimation Method for High Dimensional Generalized Structural Equation Models#617690
- Addressing Variable Heterogeneity in Distributed Multimodal Training with Entrain#238581
Record · ID 1156585
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive