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.

提出一种不需要成对样本的多视角聚类框架 SMILE，通过发现在不同视角的显著语义分布密度不变性来解决有关缺失信息的问题，从而实现了对不完整对应和不完整实例的聚类，并取得了显著的表现.