TY - JOUR
T1 - Gaussian mixture learning via adaptive hierarchical clustering
AU - Li, Jichuan
AU - Nehorai, Arye
N1 - Funding Information:
This work was supported by the AFOSR Grant No. FA9550-16-1-0386.
Publisher Copyright:
© 2018
PY - 2018/9
Y1 - 2018/9
N2 - In signal processing, a large number of samples can be generated by a Monte Carlo method and then encoded as a Gaussian mixture model for compactness in computation, storage, and communication. With a large number of samples to learn from, the computational efficiency of Gaussian mixture learning becomes important. In this paper, we propose a new method of Gaussian mixture learning that works both accurately and efficiently for large datasets. The proposed method combines hierarchical clustering with the expectation-maximization algorithm, with hierarchical clustering providing an initial guess for the expectation-maximization algorithm. We also propose adaptive splitting for hierarchical clustering, which enhances the quality of the initial guess and thus improves both the accuracy and efficiency of the combination. We validate the performance of the proposed method in comparison with existing methods through numerical examples of Gaussian mixture learning and its application to distributed particle filtering.
AB - In signal processing, a large number of samples can be generated by a Monte Carlo method and then encoded as a Gaussian mixture model for compactness in computation, storage, and communication. With a large number of samples to learn from, the computational efficiency of Gaussian mixture learning becomes important. In this paper, we propose a new method of Gaussian mixture learning that works both accurately and efficiently for large datasets. The proposed method combines hierarchical clustering with the expectation-maximization algorithm, with hierarchical clustering providing an initial guess for the expectation-maximization algorithm. We also propose adaptive splitting for hierarchical clustering, which enhances the quality of the initial guess and thus improves both the accuracy and efficiency of the combination. We validate the performance of the proposed method in comparison with existing methods through numerical examples of Gaussian mixture learning and its application to distributed particle filtering.
KW - Adaptive hierarchical clustering
KW - Adaptive splitting
KW - Distributed particle filtering
KW - Expectation-maximization algorithm
KW - Gaussian mixture learning
UR - http://www.scopus.com/inward/record.url?scp=85045675709&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2018.04.013
DO - 10.1016/j.sigpro.2018.04.013
M3 - Article
AN - SCOPUS:85045675709
SN - 0165-1684
VL - 150
SP - 116
EP - 121
JO - Signal Processing
JF - Signal Processing
ER -