Abstract
New research on neural computation is the subject of a new report. According to news reporting from the University of Mons by NewsRx journalists, research stated, "Deep nonnegative matrix factorizat ion (deep NMF) has recently emerged as a valuable technique for extracting multi ple layers of features across different scales." The news correspondents obtained a quote from the research from University of Mo ns: "However, all existing deep NMF models and algorithms have primarily centere d their evaluation on the least squares error, which may not be the most appropr iate metric for assessing the quality of approximations on diverse data sets. Fo r instance, when dealing with data types such as audio signals and documents, it is widely acknowledged that ß-divergences offer a more suitable alternative."
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