Continual multi-view clustering with pseudo-label guided consistency anchor learning.

Abstract

Continual multi-view clustering (CMVC) aims to discover cluster structures from multi-view data that arrive sequentially and exhibit distribution shifts. However, existing incremental anchor-based frameworks for CMVC still have some problems: 1. Anchors are learned in a purely reconstruction-driven way, resulting in ambiguous cluster semantics and higher sensitivity to noise. 2. Existing methods overlook the role of consistent anchor-level semantics, which limits their ability to suppress semantic drift. 3. It is tough to align the anchor graphs, weakening temporal consistency and degrading clustering stability. To address these limitations, we propose a novel continual multi-view clustering method with pseudo-label guided consistency anchor learning (CMVC-PCAL). In the initialization stage, we build a unified anchor graph representation by decomposing each anchor graph into a shared consensus component and a view-specific inconsistency component, and by introducing cluster-wise pseudo labels to align anchors with latent clusters, yielding a clearer and more noise-robust consensus anchor graph. In the continual stage, for each new data batch, a lightweight incremental scheme updates anchors and anchor graphs: an alignment matrix aligns historical and current anchors, and a consistency regularization term aligns their consensus parts, while a pseudo-label guided term jointly updates anchor pseudo labels and cluster centers to continuously refine anchor-level semantics. Finally, an alternating optimization algorithm is developed to efficiently train the model, and experiments on public multi-view benchmark datasets verify its effectiveness and efficiency in continual multi-view clustering scenarios.