Straight-Path Flow Matching for Incomplete Multi-View Clustering

Incomplete Multi-View Clustering addresses the problem ofclustering multi-modal data when certain views are missing. Recent end-to-end generative approaches leverage diffusion models to recover miss-ing views via stochastic noise-to-data trajectories. While expressive, suchmechanisms are not explicitly designed for clustering, as they initializefrom cluster-agnostic noise and rely on stochastic denoising dynamics.In this work, we revisit probability path design in end-to-end generativeIMVC. We introduce a flow-matching framework with a linear interpo-lation path between paired view representations, that replaces diffusionwith probability flows between observed and missing views. We providea formal analysis showing that deterministic ODE flows are inherentlybetter aligned with clustering objectives than diffusion-based stochastictrajectories, especially in terms of transport mechanisms that respectclass-conditional data distributions and maintain cluster consistency infinite-step regimes. Building upon this insight, we develop an end-to-end IMVC architecture that integrates straight-path flow-matching viewcompletion with cluster-level and entropy-based alignment to enforcecross-view clustering consistency. Extensive experiments on standardIMVC benchmarks demonstrate that the proposed framework establishesnew state-of-the-art performance.