@article{cfa3d19989ce4e378ac091edb27b691a,
title = "Optimizing optimization: accurate detection of hidden interactions in active body systems from noisy data",
abstract = " Given deficient and noisy movement data from a pedestrian crowd—a class of active body systems, is it possible to uncover the hidden group interaction patterns or connections? Yes, it is possible. Here, we develop a general framework based on an optimal combination of the conventional compressive sensing (L 1 minimization) and L 2 optimization procedure to achieve optimal detection of the contact network embedded in pedestrian crowd under the data shortage conditions. Different from previous publications, in our framework, the optimal weights of the L 1 and L 2 components in the combination can be determined specifically from the noisy data, which can obtain more accurate detection for the corresponding system. To detect hidden interaction patterns from spatiotemporal data has broader applications, and our optimized compressive sensing-based framework provides a practically viable solution. In addition, we provide a relative entropy perspective to facilitate more general theoretical and technological extensions of the framework. ",
keywords = "Active body system, Compressive sensing, L -regularized least squares, Optimal detection",
author = "Su, {Chun Wang} and Huang, {Zi Gang} and Wang, {Wen Xu} and Jue Wang and Wang, {Xiao Fan} and Ying-Cheng Lai",
note = "Funding Information: Acknowledgements ZGH thanks Dr. Yuzhong Chen and Prof. Tian Liu for helpful discussion. This work was supported by NSFC Nos. 11275003, 71631002, 61431012, and 11647052. XW is supported by NSFC No. 61773255. ZGH gratefully acknowledges the support of K. C. Wong Education Foundation, Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning, and Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No.17JK0553). YCL would like to acknowledge support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828. Funding Information: ZGH thanks Dr. Yuzhong Chen and Prof. Tian Liu for helpful discussion. This work was supported by NSFC Nos. 11275003, 71631002, 61431012, and 11647052. XW is supported by NSFC No. 61773255. ZGH gratefully acknowledges the support of K. C. Wong Education Foundation, Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning, and Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No.17JK0553). YCL would like to acknowledge support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828. Publisher Copyright: {\textcopyright} 2019, Springer Nature B.V.",
year = "2019",
month = apr,
day = "1",
doi = "10.1007/s11071-019-04769-1",
language = "English (US)",
volume = "96",
pages = "13--21",
journal = "Nonlinear Dynamics",
issn = "0924-090X",
publisher = "Springer Netherlands",
number = "1",
}