lanl/pyDNMFk: Python Distributed NonNegative Matrix Factorization

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<img src="https://img.shields.io/badge/-New-011B56?style=flat"></img> pyDNMFk/ Distributed pyNMFk is a software package for applying non-negative matrix factorization in a distributed memory to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence). The current implementation utilizes optimization tools such as multiplicative updates, HALS, BCD and BPP. Additionally, the Custom Clustering algorithm allows for automated determination for the number of Latent features.