Summary of Urrl-imvc: Unified and Robust Representation Learning For Incomplete Multi-view Clustering, by Ge Teng et al.
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering
by Ge Teng, Ting Mao, Chen Shen, Xiang Tian, Xuesong Liu, Yaowu Chen, Jieping Ye
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to incomplete multi-view clustering (IMVC), which addresses two main challenges: leveraging multi-view information and mitigating the impact of missing views. The existing solutions employ cross-view contrastive learning and missing view recovery techniques, but they neglect valuable complementary information or provide unreliable recovered views. To overcome these limitations, the authors introduce a Unified and Robust Representation Learning for Incomplete Multi-View Clustering (URRL-IMVC) framework that directly learns a unified embedding robust to view-missing conditions by integrating information from multiple views and neighboring samples. The framework incorporates an attention-based auto-encoder to fuse multi-view information, KNN imputation and data augmentation techniques to enhance the robustness of the unified embedding, and incremental improvements such as the Clustering Module and Encoder customization. The proposed URRL-IMVC framework is evaluated on various benchmark datasets, demonstrating its state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in clustering: what happens when you only have some of the data? Right now, we don’t know how to do this well. Most solutions try to find common things between different views of the data, but they often miss important details or make mistakes. This new approach creates a special kind of map that combines all the information from the different views and makes it robust against missing parts. It also has some extra features to help it work even better. The paper shows how well this new method does on many datasets. |
Keywords
» Artificial intelligence » Attention » Clustering » Data augmentation » Embedding » Encoder » Representation learning
