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arXiv Machine Learning By Xintong Li, Kun Zhang, Yujia Zheng

Where Latents Belong and How They Flow: Joint Identification of Structural Roles

Read the original on arXiv Machine Learning →

The paper introduces a formal definition of the structural role of latent factors in dynamical systems, describing where a latent factor is expressed, whether it receives influence from past observations, and whether it transmits influence to future observations. It addresses the challenge of correspondence across different structural identifications and proves that, under certain conditions, all three roles can be jointly identified using a common permutation at each time point. Building on this theory, the authors present RoleTrace, a framework that traces these structural roles in a shared latent coordinate system, and validate its effectiveness through synthetic experiments and real-world multiparty meeting data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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