Semantic Invariant Multi-view Clustering with Fully Incomplete Information
This addresses a practical problem in multi-view learning for real-world applications where data collection issues lead to incomplete correspondences and instances, offering a novel solution without reliance on paired samples.
The paper tackles multi-view clustering with fully incomplete information, where no paired samples are available, by proposing a framework that learns invariant semantic distributions across views, achieving clustering accuracy improvements from 19.3%/23.2% to 82.7%/69.0% on NoisyMNIST.
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.