Abstract
In this paper, we explored the possible buildup of a neural network based on Non-negative Matrix Factorization (NMF). We constructed a neural network with NMF layers that process input data in a convolutional manner. This ensures those novel layers first focus features locally and gradually expand their scope as they stack on each other. After testing we found that NMF preserves the original signal very well and has the potential to accelerate network.