Learning Intra-View Complementarity for High-Dimensional Multi-View Clustering

Multi-view clustering mainly exploits complementarity across views, while often overlooking structural diversity within each individual view. This limitation becomes more pronounced in high-dimensional settings, where heterogeneous structural cues can coexist within a single view and may not be adequately captured by a single representation. We propose LIVC, a framework for Learning Intra-View Complementarity in high-dimensional multi-view clustering. LIVC first constructs raw, global-principal, and local-structural representations to expose complementary structures within each view. It then performs granularity-aligned cross-view interaction with learnable gating for selective information transfer. Finally, residual low-rank bilinear fusion captures second-order dependencies with controlled model complexity. A joint self-supervised objective preserves view-specific information while coordinating representations across granularities and views. Experiments on multiple high-dimensional multi-view datasets demonstrate the effectiveness of LIVC for clustering.