arXiv:2606.17531v1 Announce Type: new Abstract: We investigate the learning of interpretable bases in non-negative matrix factorisation (NMF) by regularising the topology of the learned basis functions. Our approach is motivated by the observation that many data modalities can be viewed as non-negative functions on a structured domain, where the quality of a basis is intrinsically linked to its topology. However, naive methods for incorporating the topology of the support are often hindered by discreteness and threshold dependence, rendering them unsuitable for continuous optimisation. We addr
This paper introduces a novel approach to NMF by integrating topological regularisation, indicating a maturation in AI research towards more interpretable and robust models.
Improved interpretability and robustness in AI models, particularly in NMF, can enhance the reliability of AI systems across various applications, reducing black-box risks.
The ability to incorporate topological features directly into the NMF optimization process changes how researchers can develop more intuitive and data-aware AI models.
- · AI researchers
- · Machine learning model developers
- · Sectors requiring interpretable AI (e.g., medical, finance)
- · Developers of purely black-box AI models
More interpretable AI models become available for practical applications.
Increased trust and adoption of AI in sensitive domains due to enhanced explainability.
New regulatory frameworks may emerge, emphasizing interpretability as a key metric for AI deployment.
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