Contrastive Continual Multi-view Clustering with Filtered Structural Fusion: Overcoming Catastrophic Forgetting in Multi-view Clustering

Core Concepts

Proposing a method to overcome the catastrophic forgetting problem in multi-view clustering by utilizing filtered structural information.

Abstract

多視点クラスタリングにおける過去の情報を活用して、クラスタリングプロセスをガイドする方法を提案し、多視点クラスタリングにおける壊滅的な忘却問題を克服する。 Multi-view clustering is crucial for various applications, but faces challenges with real-time data and privacy issues. The proposed method, Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF), aims to address the catastrophic forgetting problem by storing filtered structural information from previous views. By utilizing this information to guide the clustering process of new views, the method shows promising results in overcoming the stability-plasticity dilemma faced by existing methods. Extensive experiments demonstrate the efficiency and effectiveness of CCMVC-FSF in improving clustering performance.

Stats

Manuscript received Mar. 3, 2024. Index Terms—Multi-view learning; Clustering; Continual learning. Extensive experiments exhibit the excellence of the proposed method. The size of the data buffer is min n2, (mp + mn) vn.

Quotes

"Given that in a clustering-induced task, the critical factor influencing the performance is the correlations among samples." "We propose a novel contrastive continual multi-view clustering method to overcome the CFP problem." "Our proposed method exceeds CMVC on most datasets."

Key Insights Distilled From

Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

by Xinhang Wan,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2309.15135.pdf

Deeper Inquiries

How can CCMVC-FSF be applied to other machine learning tasks beyond multi-view clustering

CCMVC-FSF can be applied to other machine learning tasks beyond multi-view clustering by leveraging its ability to store and utilize filtered structural information. This approach can be beneficial in tasks such as semi-supervised learning, where the stored information can guide the model in utilizing unlabeled data effectively. Additionally, in knowledge distillation tasks, the filtered structural information can act as a teacher guiding a smaller model by transferring knowledge from a larger model. Furthermore, in tasks requiring continual learning, CCMVC-FSF's method of handling catastrophic forgetting problems could prove valuable for maintaining performance on previous tasks while adapting to new data.

What are potential counterarguments against utilizing filtered structural information in guiding clustering processes

Potential counterarguments against utilizing filtered structural information in guiding clustering processes may include concerns about overfitting or bias introduced by relying too heavily on past data. Critics may argue that storing and using historical correlations could lead to models becoming less adaptable to changes in newer data patterns or distributions. There might also be challenges related to privacy and security if sensitive or outdated information is retained within the filtered structure buffer. Additionally, some researchers may question the scalability of this approach when dealing with large datasets or high-dimensional feature spaces.

How can contrastive learning be further optimized for continual multi-view clustering applications

To further optimize contrastive learning for continual multi-view clustering applications, several strategies can be considered: Adaptive Positive/Negative Sampling: Implementing dynamic sampling strategies based on sample similarities and cluster structures could enhance contrastive learning efficiency. Regularization Techniques: Introducing regularization terms specific to contrastive loss functions tailored for continual multi-view clustering could help balance partition matrix fusion and contrastive loss more effectively. Ensemble Methods: Combining multiple instances of CCMVC-FSF with different hyperparameters or initializations through ensemble methods could improve overall performance and robustness. Transfer Learning: Leveraging pre-trained models or representations from previous views as initialization points for contrastive learning in new views could accelerate convergence and enhance performance. Meta-Learning Approaches: Exploring meta-learning techniques that adaptively adjust hyperparameters during training based on task-specific characteristics could further optimize contrastive learning for continual multi-view clustering scenarios. These optimizations aim to address challenges such as stability-plasticity dilemmas, catastrophic forgetting issues, and ensuring efficient utilization of prior knowledge while adapting to new data streams seamlessly.