Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method

By Xuelong Li, Rong Wang, Feiping Nie. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

434 citations recorded by the source corpus.

Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a2ae1cee09136df04f8746232860106b2fcbc463

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. En Zhu
2. Siwei Wang
3. Xinwang Liu
4. Pei Zhang
5. Yi Zhang
6. Feng Gao
7. Xinzhong Zhu

In Computer Vision and Pattern Recognition

1. En Zhu
2. Siwei Wang
3. Xinwang Liu
4. Pei Zhang
5. Yi Zhang
6. Feng Gao
7. Xinzhong Zhu

In Media Technology

1. En Zhu
2. Xinwang Liu
3. Pei Zhang

Citations in the corpus

• Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, methodology): “propose an alternate anchor sampling strategy to build individual anchor graphs and then combine them into the consensus graph [12].”