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