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.