Straight-Path Flow Matching for Incomplete Multi-View Clustering

• URL: http://arxiv.org/abs/2607.06281v1
• Date: Tue, 07 Jul 2026 13:47:51 GMT
• Title: Straight-Path Flow Matching for Incomplete Multi-View Clustering
• Abstract summary: Incomplete Multi-View Clustering addresses the problem of clustering multi-modal data when certain views are missing.<n>Recent approaches leverage diffusion models to recover missing views via noise-to-data trajectories.<n>We develop an end-to-end IMVC architecture that integrates flow-matching view completion with cluster-level and entropy-based alignment.
• Score: 7.71004905661976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 representations, that replaces diffusion with probability flows between observed and missing views. We provide a formal analysis showing that deterministic ODE flows are inherently better aligned with clustering objectives than diffusion-based stochastic trajectories, especially in terms of transport mechanisms that respect class-conditional data distributions and maintain cluster consistency in finite-step regimes. Building upon this insight, we develop an end-to-end IMVC architecture that integrates straight-path flow-matching view completion with cluster-level and entropy-based alignment to enforce cross-view clustering consistency. Extensive experiments on standard IMVC benchmarks demonstrate that the proposed framework establishes new state-of-the-art performance.

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