Robust Multi-View Clustering With Incomplete Information

By Mouxing Yang, Yunfan Li, Peng Hu, Jinfeng Bai, Jiancheng Lv, Xi Peng. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

355 citations recorded by the source corpus.

The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework. In brief, SURE is a novel contrastive learning paradigm which uses the available pairs as positives and randomly chooses some cross-view samples as negatives. To reduce the influence of the false negatives caused by random sampling, SURE is with a noise-robust contrastive loss that theoretically and empirically mitigates or even eliminates the influence of the false negatives. To the best of our knowledge, this could be the first successful attempt that simultaneously handles PVP and PSP using a unified solution. In addition, this could be one of the first studies on the noisy correspondence problem (i.e., the false negatives) which is a novel paradigm of noisy labels. Extensive experiments demonstrate the effectiveness and efficiency of SURE comparing with 10 state-of-the-art approaches on the multi-view clustering task.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/43e08ca5d63fb3517d33ae42373ddf8f35758cdf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lifang He

In Computer Vision and Pattern Recognition

1. Liang Peng
2. Yazhou Ren
3. Huayi Tang
4. Jie Xu
5. Lifang He
6. Xiaofeng Zhu

Citations in the corpus

• Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, background): “Multi-view clustering (MVC) is attracting more and more attention in recent years [22,50,52,57] as multi-view data or multi-modal data can provide common semantics to improve the learning effectiveness [3, 14, 27, 33, 36, 43].”
• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, neutral): “like clustering [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], classification [14], [15], [16], [17], and human action recognition [18].”