Doubly Aligned Incomplete Multi-view Clustering

By Menglei Hu, Songcan Chen. International Joint Conference on Artificial Intelligence, 2018

317 citations recorded by the source corpus.

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of  L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Urban Studies

View Paper: https://www.semanticscholar.org/paper/f7beac7bd14436cbf4c9286fc6c81a28e95affc4

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. Peng Hu
2. Xi Peng
3. Boyun Li
4. Yuanbiao Gou
5. Jiancheng Lv
6. Yunfan Li
7. Jinfeng Bai
8. Mouxing Yang

In Computer Vision and Pattern Recognition

1. Peng Hu
2. Xi Peng
3. Boyun Li
4. Yuanbiao Gou
5. Jiancheng Lv
6. Yunfan Li
7. Jinfeng Bai
8. Mouxing Yang

In Urban Studies

1. Xi Peng
2. Yuanbiao Gou
3. Jiancheng Lv
4. Jinfeng Bai
5. Yijie Lin

Citations in the corpus

• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, methodology): “(8) DAIMC (Doubly Aligned Incomplete Multi-view Clustering) [25] learns a common space using non-negative matrix factorization and L2;1-Norm regularized regression.”
• Robust Multi-View Clustering With Incomplete Information (2022, methodology): “We compare SURE with 10 multi-view clustering baselines including CCA [12], KCCA [11], DCCA [1], DCCAE [2], BMVC [14], AE(2)-Nets [4], PMVC [51], UEAF [58], DAIMC [16] and EERIMC [17].”
• COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (2021, methodology): “Second, our method is a deep rather than shallow model [10, 19, 22, 26, 35, 39, 53], thus naturally embracing the capacity of handling complex and large-scale dataset.”