AF-UMC: An Alignment-Free Fusion Framework for Unaligned Multi-View Clustering
Bohang Sun, Yuena Lin, Tao Yang, Zhen Zhu, Zhen Yang, Gengyu Lyu
The Unaligned Multi-view Clustering (UMC) aims to learn a discriminative cluster structure from unaligned multi-view data, where the features of samples are not completely aligned across multiple views. Most of current methods usually prioritize employing various alignment strategies to align different sample representations and then conduct cross-view fusion for subsequent clustering. However, ***due to the heterogeneity of representations across different views, these alignment strategies often fail to achieve ideal view-alignment results, inevitably leading to unreliable cross-view fusion.*** To address this issue, we propose an alignment-free consistency fusion framework named AF-UMC, which bypasses the traditional view-alignment operation and directly extracts consistent representation from each view to perform global cross-view consistency fusion. Specifically, we first construct a cross-view consistent basis space by a cross-view reconstruction loss and a designed Structural Clarity Regularization (SCR), where autoencoders extract consistent representation of each view through projecting view-specific data to the constructed basis space. Afterwards, these extracted representations are globally pulled together for further cross-view fusion according to a designed Instance Global Contrastive Enhancement (IGCE), which makes the fused consistent representation with higher global consistency. Compared with previous methods, AF-UMC directly extracts consistent representation from each view for global fusion rather than alignment for fusion, which significantly mitigates the degraded performance caused by undesired view-alignment results while greatly reduces algorithm complexity and enhances its efficiency. Extensive experiments on various datasets demonstrate that our proposed method exhibits superior performance against other state-of-the-art methods.