Semantic-Guided Cross-View Latent Transport via Flow Matching for Incomplete Multi-View Clustering

This paper presents TRACE, a semantic-guided cross-view latent transport frame- work via conditional flow matching for incomplete multi-view clustering. Specifi- cally, TRACE first learns a stable dual-level representation that separates compact semantic codes from view-specific complete latent representations, providing clustering-oriented semantic conditions for cross-view transport while preserving view-specific information. Building on these semantic conditions, we introduce a target-excluded conditional flow matching strategy that temporarily treats an observed view as a pseudo-missing target and learns source-to-target transport routes in the frozen latent space without accessing target-view information during conditioning. Furthermore, TRACE employs an observed-priority multi-source recovery mechanism, in which each missing view is independently recovered from all authentic observed views and the resulting target candidates are aggregated, while generated views are prevented from participating in subsequent recovery. This design avoids recursive error propagation and preserves the information of genuinely observed views. Extensive experiments on five datasets demonstrate that TRACE achieves competitive or superior performance against recent state-of-the- art methods.