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Non-Negative Matrix Factorization (NMF) is a valuable matrix factorizationtechnique which produces a "parts-based" decomposition of data sets. Wi-Fi usercounts are a privacy-preserving indicator of population movements in smart andconnected urban environments. In this paper, we apply NMF with a novel matrixembedding to Wi-Fi user count data from the University of Colorado at BoulderCampus for the purpose of automatically identifying patterns of human movementin a Smart and Connected infrastructure environment.

Identifying Population Movements with Non-Negative Matrix Factorization from Wi-Fi User Counts in Smart and Connected Cities