Reordered $k$-Means: A New Baseline for View-Unaligned Multi-View Clustering

Zhiqiang Fu, Yao Zhao, Dongxia Chang, Yiming Wang, Jie Wen

Abstract: Most current multi-view clustering methods necessitate that a sample's features be view-aligned or at least partially aligned across different views. Regrettably, real-world applications often fail to meet this requirement due to spatial, temporal, or spatiotemporal mismatches, resulting in the view-unaligned issue. To tackle this issue, we conceptualize the view-unaligned problem and demonstrate that it can be transformed into a view-aligned problem through reordering. Building on this concept, we introduce an innovative reorder matrix that realigns view-unaligned features. Utilizing these realigned features, we develop a sophisticated and efficient approach called Reordered $k$-means (RKM), which merges NMF with $k$-means. Unlike traditional $k$-means, our method converts the binary challenge into an $\ell _{0}$ problem, confirming the merit of this advancement. Furthermore, RKM's efficacy is affirmed on benchmarks, indicating substantial enhancements in handling the view-unaligned issue and maintaining competitive results with view-aligned problems.