Meta-learning is originally developed for supervised learning to enable models to generalize across tasks by leveraging prior experience. However, its potential in the unsupervised learning domain remains largely underexplored. To demonstrate the feasibility of meta-learning in unsupervised settings, we apply it to Non-negative Matrix Factorization (NMF), a widely used technique for decomposing non-negative data matrices into interpretable, lower-dimensional representations. While NMF has found applications in topic modeling, image processing, bioinformatics, and recommendation systems, it faces persistent challenges such as rank selection, optimization stability, uniqueness, and computational efficiency. Traditional approaches primarily focus on improving initialization strategies to enhance convergence and generalization. However, these methods often fail to exploit structural similarities across tasks. In this paper, we introduce a meta-learning paradigm for NMF that systematically learns optimal factorization parameters from small-scale tasks and transfers this knowledge to improve learning on larger tasks. By discovering fine structures in small tasks and leveraging them to guide factorization on more complex datasets, our approach directs the search process toward a more optimal and structured search space, reducing the risk of suboptimal solutions and improving model robustness. This meta-unsupervised learning framework enhances NMF's ability to uncover meaningful patterns while maintaining adaptability across different domains. Additionally, we evaluate the model under noisy conditions and demonstrate its robustness by filtering noise over learning epochs, further enhancing its interpretability and stability. By integrating meta-learning principles, our method not only improves optimization stability but also enhances interpretability and generalizability, bridging the gap between NMF-based models and advanced autonomous (unsupervised) learning strategies.
Khan et al. (Mon,) studied this question.