Jeffrey's divergence between moving-average models that are real or complex, noise-free or disturbed by additive white noises
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- 作者：L. Legranda ; b ; leo.legrand@u-bordeaux.fr" class="auth_mail" title="E-mail the corresponding author ; E. Grivelb
- 关键词：Jeffrey's divergence ; Moving-average models ; Higher-order singular value decomposition ; Tensor analysis
- 刊名：Signal Processing
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：131
- 期：Complete
- 页码：350-363
- 全文大小：6421 K
文摘
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Analytical Jeffrey's Divergence expressions are provided for 1st-order MA models.
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Real or complex, noise-free or noisy 1st-order MA model cases are studied.
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Time evolution of the Jeffrey's Divergence depends on the 1st-order MA parameters.
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Comparative study with other dissimilarity measures (Rao distance, etc.) is done.
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Two methods are proposed to compare more than two 1st-order MA models.