Yes, the iN-NMF approach can be adapted to work with other matrix factorization techniques beyond NMF, such as sparse coding or probabilistic matrix factorization, by modifying the formulation of the implicit neural representations and the optimization process. Here's how it can be done:
Sparse Coding: For sparse coding, the implicit neural representations need to incorporate sparsity-inducing mechanisms in the neural networks. This can be achieved by adding regularization terms or using specific activation functions that promote sparsity in the learned functions. The optimization algorithm would need to be tailored to enforce sparsity constraints during training.
Probabilistic Matrix Factorization: To adapt iN-NMF to probabilistic matrix factorization, the implicit neural representations should be designed to model the probabilistic distributions of the latent factors. This involves incorporating probabilistic layers or loss functions in the neural networks to capture uncertainty in the factorization process. The optimization procedure would then involve maximizing the likelihood of the observed data under the probabilistic model.
By customizing the implicit neural representations and optimization strategies, the iN-NMF framework can be extended to accommodate a variety of matrix factorization techniques, allowing for more versatile and powerful signal processing applications beyond traditional NMF.