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.
| Original language | English (US) |
|---|---|
| Journal | Nonlinear Dynamics |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- Active body system
- Compressive sensing
- L -regularized least squares
- Optimal detection
ASJC Scopus subject areas
- Control and Systems Engineering
- Aerospace Engineering
- Ocean Engineering
- Mechanical Engineering
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Optimizing optimization : accurate detection of hidden interactions in active body systems from noisy data. / Su, Chun Wang; Huang, Zi Gang; Wang, Wen Xu; Wang, Jue; Wang, Xiao Fan; Lai, Ying-Cheng.
In: Nonlinear Dynamics, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimizing optimization
T2 - accurate detection of hidden interactions in active body systems from noisy data
AU - Su, Chun Wang
AU - Huang, Zi Gang
AU - Wang, Wen Xu
AU - Wang, Jue
AU - Wang, Xiao Fan
AU - Lai, Ying-Cheng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Active body system
KW - Compressive sensing
KW - L -regularized least squares
KW - Optimal detection
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UR - http://www.scopus.com/inward/citedby.url?scp=85062015470&partnerID=8YFLogxK
U2 - 10.1007/s11071-019-04769-1
DO - 10.1007/s11071-019-04769-1
M3 - Article
JO - Nonlinear Dynamics
JF - Nonlinear Dynamics
SN - 0924-090X
ER -