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.