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 remains 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. We will release the code after acceptance.

翻译：针对真实多视角应用中普遍存在的对应不完整与实例缺失问题，不完整信息下的鲁棒多视角学习已受到广泛关注。现有方法严重依赖配对样本对缺陷样本进行重对齐或插补，但由于数据采集与传输的复杂性，此类前提在实际中往往难以满足。为解决该问题，我们提出了一种名为语义不变学习（SMILE）的新型框架，用于无需任何配对样本的不完整多视角聚类。具体而言，我们发现了不同视角间存在不变的语义分布，这使得SMILE能够在无需配对样本的情况下缓解跨视角差异以学习共识语义。所得到的共识语义不受跨视角分布偏移的影响，从而可用于缺陷实例的重对齐/插补并形成聚类。通过在五个基准上与13种最先进基线方法进行广泛对比实验，我们验证了SMILE的有效性。当对应关系/实例完全缺失时，本方法将NoisyMNIST的聚类准确率从19.3%/23.2%提升至82.7%/69.0%。论文接收后我们将公开代码。