Identifying Population Movements with Non-Negative Matrix Factorization from Wi-Fi User Counts in Smart and Connected Cities
Machine Learning
2021-11-23 v1 Signal Processing
Abstract
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.
Cite
@article{arxiv.2111.10459,
title = {Identifying Population Movements with Non-Negative Matrix Factorization from Wi-Fi User Counts in Smart and Connected Cities},
author = {Michael Huffman and Armen Davis and Joshua Park and James Curry},
journal= {arXiv preprint arXiv:2111.10459},
year = {2021}
}