Distributed learning of human mobility patterns from cellular network data

The advent of ubiquitous mobile devices has provided us with an abundant spatio-temporal data source that helps us understand human mobility. The big data generated from mobile devices can be distributed at different locations and it is always infeasible to aggregate the data from multiple data collection centers into one location due to communication and privacy considerations. This paper studies human mobility patterns by learning data-adaptive representations for cellular network data that are distributed across a set of interconnected nodes. It proposes a distributed algorithm, termed cloud NN-K-SVD, for collaboratively learning a sparsifying dictionary (i.e., overcomplete basis) from the data without exchanging data samples between different nodes. The effectiveness of cloud NN-K-SVD is demonstrated through experiments on anonymized Call Detail Records from Columbus, OH.

[1]  Waheed Uz Zaman Bajwa,et al.  Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data , 2014, IEEE Transactions on Signal Processing.

[2]  Franz Pernkopf,et al.  Sparse nonnegative matrix factorization using ℓ0-constraints , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[3]  Arindam Banerjee,et al.  Online (cid:96) 1 -Dictionary Learning with Application to Novel Document Detection , 2012 .

[4]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[5]  Margaret Martonosi,et al.  ON CELLULAR , 2022 .

[6]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  D. Donoho,et al.  Sparse nonnegative solution of underdetermined linear equations by linear programming. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[9]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[10]  Junli Liang,et al.  Distributed Dictionary Learning for Sparse Representation in Sensor Networks , 2014, IEEE Transactions on Image Processing.

[11]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[12]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[13]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[14]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[15]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[16]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[17]  Michael Elad,et al.  K-SVD and its non-negative variant for dictionary design , 2005, SPIE Optics + Photonics.

[18]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.