Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation

• URL: http://arxiv.org/abs/2209.11927v1
• Date: Sat, 24 Sep 2022 05:08:34 GMT
• Title: Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation
• Authors: Jiatai Wang, Zhiwei Xu, Xuewen Yang, Dongjin Guo, Limin Liu
• Abstract summary: We propose Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN) We incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Experiments conducted on textcolorblackfour extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.
• Score: 5.314364096882052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: 1) It's difficult to learn latent representations that account for complementarity yet consistency without using label information; 2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this paper, we propose Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalize the model in the presence of high data missing rates. Experiments conducted on \textcolor{black}{four} extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.

Related papers

• Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment [13.511433241138702]
  Partial multi-view clustering (PVC) presents significant challenges practical research problem for data analysis in real-world applications. Existing clustering methods struggle to handle incomplete views effectively, leading to suboptimal clustering performance. We propose a novel dual optimization framework based on contrastive learning, which aims to maximize the consistency of latent features in incomplete multi-view data.
  arXiv  Detail & Related papers  (2024-11-14T19:16:01Z)
• CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network [53.72046586512026]
  We propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net) It captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training.
  arXiv  Detail & Related papers  (2024-03-28T15:45:03Z)
• Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding [7.305817202715752]
  We propose a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC) Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information.
  arXiv  Detail & Related papers  (2023-12-14T07:28:41Z)
• Scalable Incomplete Multi-View Clustering with Structure Alignment [71.62781659121092]
  In this paper, we propose a novel incomplete anchor graph learning framework. We construct the view-specific anchor graph to capture the complementary information from different views. The time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples.
  arXiv  Detail & Related papers  (2023-08-31T08:30:26Z)
• Unpaired Multi-View Graph Clustering with Cross-View Structure Matching [39.310384044597065]
  Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in advance. The data correspondence is often incomplete in real-world applications due to data corruption or sensor differences. We propose a novel parameter-free graph clustering framework termed Unpaired Multi-view Graph Clustering framework with Cross-View Structure Matching.
  arXiv  Detail & Related papers  (2023-07-07T09:29:44Z)
• Incomplete Multi-view Clustering via Diffusion Completion [0.0]
  We propose diffusion completion to recover the missing views integrated into an incomplete multi-view clustering framework. Based on the observable views information, the diffusion model is used to recover the missing views. The proposed method performs well in recovering the missing views while achieving superior clustering performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-19T07:39:24Z)
• Clustering-Induced Generative Incomplete Image-Text Clustering (CIGIT-C) [3.2062075983668343]
  We propose a Clustering-Induced Generative Incomplete Image-Text Clustering(CIGIT-C) network to address the challenges above. We first use modality-specific encoders to map original features to more distinctive subspaces. The latent connections between intra and inter-modalities are thoroughly explored.
  arXiv  Detail & Related papers  (2022-09-28T01:19:52Z)
• Latent Heterogeneous Graph Network for Incomplete Multi-View Learning [57.49776938934186]
  We propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks.
  arXiv  Detail & Related papers  (2022-08-29T15:14:21Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities [67.54503077766171]
  Since label information is often expensive to acquire, multi-view clustering has gained growing interest. Error-robust multi-view clustering approaches with explicit error removal formulation can be structured into five broad research categories. This survey summarizes and reviews recent advances in error-robust clustering for multi-view data.
  arXiv  Detail & Related papers  (2021-05-07T04:03:02Z)
• Generative Partial Multi-View Clustering [133.36721417531734]
  We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
  arXiv  Detail & Related papers  (2020-03-29T17:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.