Cross-View Prototype Calibration via Reciprocal Cyclic Transitions for Incomplete and Noisy Multi-View Clustering

Multi-view clustering aims to uncover a consensus cluster structure from heterogeneous views of the same objects. Prototype-based methods summarize the clusters of each view with semantic prototypes and align them across views. However, missing views and feature noise distort local neighborhood structures and shift view-specific prototypes, rendering cross-view correspondences ambiguous and prone to many-to-one matches. Establishing reliable and reciprocal prototype correspondences under such degradation therefore remains challenging. We propose ReCAP, an imputation-free framework for Reciprocal Cyclic cAlibration of cross-view Prototypes under incomplete and noisy settings. ReCAP first applies mutual k-nearest-neighbor graph contrastive learning to preserve reliable local neighborhoods and stabilize prototype estimation. It then constructs sample-mediated cyclic transitions that propagate probability mass from each source prototype through the samples and prototypes of another view and back to the source view. Maximizing the probability of returning to the source prototype couples forward and backward transitions; we prove that, in the zero-loss limit, the two directional transitions reduce to a permutation and its inverse, i.e., a reciprocal one-to-one correspondence. A visitation regularizer further prevents the returned mass from collapsing onto a few prototypes. Experiments on six benchmark datasets demonstrate competitive or superior clustering performance under incomplete, noisy, and compound degradation settings, while further analyses support the effectiveness of cyclic prototype calibration. Our code is available at https://anonymous.4open.science/r/ReCAP-0301.