首页|University of Mons Researcher Publishes Findings in Neural Computation (Deep Non negative Matrix Factorization with Beta Divergences)
University of Mons Researcher Publishes Findings in Neural Computation (Deep Non negative Matrix Factorization with Beta Divergences)
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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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