基于双面亲和学习与拓扑协调的即插即用不完整多视图聚类
Plug-and-Play Incomplete Multi-View Clustering via Janus-Faced Affinity Learning with Topology Harmonization
锚图上的置换变换加一元编码约束，可在任意多视图/多模态特征融合前对齐锚点拓扑，这一思路可直接迁移到多视图特征拼接、跨模态检索的相似度融合环节，避免因视图间锚点顺序不一致导致的图结构失真。
原文依据 a permutation transformation with unary encoding constraints is applied to anchor matrix, realigning anchor topology while preserving the values
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现有的不完整多视图聚类（IMVC）方法在学习视图一致性表示时，通常未能考虑视图独有伪影的干扰，这可能损害所得相似度度量的保真度。此外，各视图间锚框顺序的不一致可能扭曲图结构，从而削弱聚类性能。对需精细调参的正则化超参数的依赖，也通常削弱了模型的实用性。为缓解上述问题，我们提出一个名为 PJFTH 的即插即用 IMVC 框架，它将双面亲和学习与拓扑协调相结合。该框架显式建模「视图独有—视图共识」之间的相互作用，从每个视图导出视图私有图，并依据各视图自身的内在特性将其自适应地整合为全局共识亲和。进一步地，对锚框矩阵施加带一元编码约束的置换变换，在保持数值不变的同时重新对齐锚框拓扑。该过程在相似度整合之前同步锚框顺序，并保持原始锚框性质。值得注意的是，所有组件无缝耦合并以联合方式优化。此外，可证明的整体线性复杂度进一步提升了其可扩展性与实用性。实验结果证实，PJFTH 相较若干领先方法取得了有竞争力的性能。
英文原文摘要 1446 字符
Prevailing incomplete multi-view clustering (IMVC) approaches typically fail to account for the interference of view-exclusive artifacts when learning view-consensus representations, which could compromise the fidelity of the resulting similarity measure. Moreover, inconsistencies in anchor order across views may distort the graph structure, impairing the clustering performance. The reliance on carefully-tuned regularization hyper-parameters also usually undermines the model's practical utility. To alleviate these issues, we propose a plug-and-play IMVC framework named PJFTH that incorporates Janus-faced affinity learning with topology harmonization. It explicitly models the exclusive-to-consensus interplay, derives a view-private graph from each view, and adaptively integrates them into a global consensus affinity according to the respective view's intrinsic characteristics. Furthermore, a permutation transformation with unary encoding constraints is applied to anchor matrix, realigning anchor topology while preserving the values. This process synchronizes anchor order prior to similarity integration and maintains original anchor properties. Notably, all components are coupled seamlessly and optimized in a joint manner. Also, the provable overall linear complexity further enlarges its scalability and practicality. Experimental results confirm that PJFTH receives competitive performance compared to several leading methods.