解决多视图数据缺失下的聚类问题，提升聚类准确性和视图一致性。
Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment
- 分层补全：先预测缺失视图的聚类归属，再用组内统计重建特征。
- 在多个数据集上优于现有方法，缺失率越高优势越明显。
- 适合处理不完整多视图数据的场景，如医疗、图像分析。
不完全多视图聚类（IMVC）旨在从存在部分观测缺失的多视图数据中发现共享的聚类结构。核心挑战在于：在不引入偏差的前提下准确补全缺失视图，同时保持视图间的语义一致性与簇内紧凑性。为此，我们提出DIMVC-HIA，一种融合分层补全与对齐的深度IMVC框架，包含四个关键组件：(1) 视图特定的自编码器提取潜在特征，结合视图共享聚类预测器生成软聚类分配；(2) 分层补全模块：先基于跨视图对比相似性估计缺失聚类分配，再利用视图内、簇内统计信息重建缺失特征；(3) 基于能量的语义对齐模块，通过最小化低能量簇锚点周围的能量方差来增强簇内紧凑性；(4) 对比聚类分配对齐模块，提升跨视图一致性并促进高置信度、高分离度的聚类预测。在基准数据集上的实验表明，该框架在不同缺失水平下均表现优异。
原文摘要 · Abstract (English)
Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using intra-view, intra-cluster statistics; (3) an energy-based semantic alignment module, which promotes intra-cluster compactness by minimizing energy variance around low-energy cluster anchors; and (4) a contrastive assignment alignment module, which enhances cross-view consistency and encourages confident, well-separated cluster predictions. Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
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