SCoGF: Spectral Consensus Graph Fusion for Scalable Incomplete Multi-view Clustering

Spectral multi-view clustering under view-level missingness faces an unresolved tension: scalable methods rely on kernel evaluations that are undefined when a sample lacks a view, while incomplete-view methods either impute missing views and propagate recovery error, or couple all $N$ samples in every iteration at cubic cost. We propose SCoGF (Spectral Consensus Graph Fusion), a single-pass method in which the missing masks shape the graph construction directly rather than entering through any later correction. SCoGF learns per-view anchor graphs from simplex-constrained reconstruction weights on observed anchors only, fuses them by a uniform masked average that is the exact minimizer of the masked aggregation subproblem, and embeds all samples through a randomized SVD with a missing-aware cross-anchor extension. Deterministic $k$-means anchor selection makes the output reproducible given the masks. The pipeline is imputation-free, non-iterative, free of kernel bandwidths and learned view weights, and near-linear in $N$. Theoretically, we prove that the masked average is statistically sound with variance decaying in the view count, the anchor embedding is stable under explicit budgets on the anchor and view counts, and the resulting partition admits an end-to-end normalized-cut consistency bound. Empirically, across multi-view benchmarks under both symmetric and view-dependent missingness, SCoGF is the only compared method that never collapses on feature-heterogeneous data, while running orders of magnitude faster than iterative competitors and requiring no per-rate tuning.