Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remain unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3\%/23.2\% to 82.7\%/69.0\% when the correspondences/instances are fully incomplete. The code could be accessed from https://pengxi.me.

翻译：针对实际多视图应用中常见的对应不完全与实例不完全问题，鲁棒性不完全信息多视图学习已受到广泛关注。现有方法高度依赖成对样本来校正或补全缺陷样本，但由于数据采集与传输的复杂性，此类前提条件在实践中往往难以满足。为解决该问题，我们提出一种无需任何成对样本的全新框架——语义不变学习（SMILE），用于不完全信息多视图聚类。具体而言，我们发现不同视图间存在不变的语义分布，这使得SMILE能够在不依赖成对样本的情况下缓解跨视图差异，学习共识语义。所获得的共识语义不受跨视图分布偏移影响，因此可用于校正/补全缺陷实例并形成聚类簇。通过与13个最先进基线方法在五个基准数据集上的广泛对比实验，我们验证了SMILE的有效性。当对应关系/实例完全不完全时，该方法将NoisyMNIST数据集的聚类准确率从19.3%/23.2%提升至82.7%/69.0%。代码可通过https://pengxi.me 获取。