Federated multi-view clustering offers the potential to develop a global
clustering model using data distributed across multiple devices. However,
current methods face challenges due to the absence of label information and the
paramount importance of data privacy. A significant issue is the feature
heterogeneity across multi-view data, which complicates the effective mining of
complementary clustering information. Additionally, the inherent incompleteness
of multi-view data in a distributed setting can further complicate the
clustering process. To address these challenges, we introduce a federated
incomplete multi-view clustering framework with heterogeneous graph neural
networks (FIM-GNNs). In the proposed FIM-GNNs, autoencoders built on
heterogeneous graph neural network models are employed for feature extraction
of multi-view data at each client site. At the server level, heterogeneous
features from overlapping samples of each client are aggregated into a global
feature representation. Global pseudo-labels are generated at the server to
enhance the handling of incomplete view data, where these labels serve as a
guide for integrating and refining the clustering process across different data
views. Comprehensive experiments have been conducted on public benchmark
datasets to verify the performance of the proposed FIM-GNNs in comparison with
state-of-the-art algorithms.

通过引入具有异质图神经网络的不完整多视图聚类框架（FIM-GNNs），我们解决了由于缺乏标签信息和数据隐私的重要性而导致的异构特征数据的有效挖掘困难，以及分布式环境中多视图数据的固有不完整性使得聚类过程复杂化的问题。

异构图神经网络下的联邦多视角聚类

Federated Incomplete Multi-View Clustering with Heterogeneous Graph  Neural Networks

Although multi-view unsupervised feature selection (MUFS) is an effective
technology for reducing dimensionality in machine learning, existing methods
cannot directly deal with incomplete multi-view data where some samples are
missing in certain views. These methods should first apply predetermined values
to impute missing data, then perform feature selection on the complete dataset.
Separating imputation and feature selection processes fails to capitalize on
the potential synergy where local structural information gleaned from feature
selection could guide the imputation, thereby improving the feature selection
performance in turn. Additionally, previous methods only focus on leveraging
samples' local structure information, while ignoring the intrinsic locality of
the feature space. To tackle these problems, a novel MUFS method, called
UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed.
UNIFIER explores the local structure of multi-view data by adaptively learning
similarity-induced graphs from both the sample and feature spaces. Then,
UNIFIER dynamically recovers the missing views, guided by the sample and
feature similarity graphs during the feature selection procedure. Furthermore,
the half-quadratic minimization technique is used to automatically weight
different instances, alleviating the impact of outliers and unreliable restored
data. Comprehensive experimental results demonstrate that UNIFIER outperforms
other state-of-the-art methods.

多视图无监督特征选择方法 UNIFIER 能够处理不完整的多视图数据，并通过学习相似性诱导图动态恢复缺失视图，提高特征选择性能。

不完整的多视图数据的统一视图填补和特征选择学习

Unified View Imputation and Feature Selection Learning for Incomplete  Multi-view Data

Classifying incomplete multi-view data is inevitable since arbitrary view
missing widely exists in real-world applications. Although great progress has
been achieved, existing incomplete multi-view methods are still difficult to
obtain a trustworthy prediction due to the relatively high uncertainty nature
of missing views. First, the missing view is of high uncertainty, and thus it
is not reasonable to provide a single deterministic imputation. Second, the
quality of the imputed data itself is of high uncertainty. To explore and
exploit the uncertainty, we propose an Uncertainty-induced Incomplete
Multi-View Data Classification (UIMC) model to classify the incomplete
multi-view data under a stable and reliable framework. We construct a
distribution and sample multiple times to characterize the uncertainty of
missing views, and adaptively utilize them according to the sampling quality.
Accordingly, the proposed method realizes more perceivable imputation and
controllable fusion. Specifically, we model each missing data with a
distribution conditioning on the available views and thus introducing
uncertainty. Then an evidence-based fusion strategy is employed to guarantee
the trustworthy integration of the imputed views. Extensive experiments are
conducted on multiple benchmark data sets and our method establishes a
state-of-the-art performance in terms of both performance and trustworthiness.

提出了一个基于不确定性的不完整多视图数据分类模型，通过构建分布来捕捉丢失视图的不确定性，并根据采样质量自适应利用它们来实现更明显的数据填充和可控融合，使用证据融合策略来保证集成填充视图的可靠性和性能，多个基准数据集的实验证明了该方法的优越性。