Straight-Path Flow Matching for Incomplete Multi-View Clustering
Incomplete Multi-View Clustering addresses the problem of clustering multi-modal data when certain views are missing. Recent end-to-end generative approaches leverage diffusion models to recover missing views via stochastic noise-to-data trajectories. While expressive, such mechanisms are not explicitly designed for clustering, as they initialize from cluster-agnostic noise and rely on stochastic denoising dynamics. In this work, we revisit probability path design in end-to-end generative IMVC. We introduce a flow-matching framework with a linear interpolation path between paired view represen
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- Linked via arxiv authorYiteng Yuan →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorJunyan Wang →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorZheyuan Liu →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorHong Jia →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorLei Fan →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorZhulin Tao →
Straight-Path Flow Matching for Incomplete Multi-View Clustering
- Linked via arxiv authorLianbo Guo →
Straight-Path Flow Matching for Incomplete Multi-View Clustering