Journal
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
Volume 89, Issue -, Pages 130-141Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcss.2017.05.010
Keywords
Big data; Remote sensing; Correlation analysis; Sparse representation
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As a typical representative of big data, geographic spatiotemporal big data present new features especially the non-stationary feature, bringing new challenges to mine correlation information. However, representation of instantaneous information is the main bottleneck for non-stationary data, but the traditional non-stationary analysis methods are limited by Heisenberg's uncertainty principle. Therefore, we firstly represent instantaneous frequency of geographic spatiotemporal big data based on Hilbert-Huang transform to overcome traditional methods' weakness. Secondly, we propose absolute entropy correlation analysis method based on KL divergence. Finally, we select five geographic factors to certify that the absolute entropy correlation analysis method is effective and distinguishable. (C) 2017 Elsevier Inc. All rights reserved.
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