Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1999 by Lee and Seung, including in compression of data, document clustering, processing of audio spectrograms and astronomy. In this work we have adapted a minimization scheme for convex functions with non-differentiable constraints called PALM to solve the NMF problem with solutions that can be smooth and/or sparse, two properties frequently desired.
翻译:非负矩阵因子化是一个维度减少和数据源分离的问题,自1999年Lee和Leung对数据进行深入研究以来,许多领域广泛使用这些数据,包括数据压缩、文件集群、音频光谱和天文学处理,在这项工作中,我们调整了一项最小化计划,用非区别的制约,即PALM, 解决NMF问题,其解决办法可以是平滑的和/或稀少的,经常需要两种特性。