It can be found that JSD shows better robustness than Kullback–Leibler divergence (KLD) on quantifying time series irreversibility and correctly distinguishes the different type of simulated series. For the empirical analysis, JSD based on HVG is able to detect the significant time irreversibility of...
This paper describes the Jensen-Shannon divergence (JSD) and Hilbert space embedding. With natural definitions making these considerations precise, one finds that the general Jensen-Shannon divergence related to the mixture is the minimum redundancy, which can be achieved by the observer. The set of...
Jensen-Shannon Divergence and Hilbert space embedding Bent Fuglede and Flemming Topsøe University of Copenhagen, Department of Mathematics Consider the set M 1 + (A) of probability distributions where A is a set provided with some σ-algebra. Jensen-Shannon divergence JSD : M 1 + (A) × M...
As shown in the Wikipedia page, the Kullback-Leibler divergence and the Hellinger Distance can be expressed using only the means and standard deviations - that is, without the variable xx. Can the JSD be expressed in a similar way? normal-distribution distance distance-functions ...
This paper describes the Jensen-Shannon divergence (JSD) and Hilbert space embedding. With natural definitions making these considerations precise, one fin... F Topsøe,B Fuglede - International Symposium on Information Theory 被引量: 323发表: 2005年 On the metric character of the quantum Jensen...
The present work is devoted to review the main properties of a distance known as the Jensen‐Shannon divergence (JSD) in its classical and quantum version. We present two examples of application of this distance: in the first one we use it as a quantifiers of the stochastic resonance ...
The kernel for structures p and q is positive definite (pd) with the following kernel function kJSK (Pp, Pq) = log 2 − J SD(Pp, Pq) (2) where JSD(Pp, Pq) is the Jensen-Shannon divergence between the probability distributions Pp and Pq defined as J SD(Pp, Pq) = HS( Pp +...
例如:然后,该函数应采用kl_divergence(X, X)并计算两个X矩阵的每一对行的成对Kl散度距离。输出将是一个2x2矩阵。 如果不是,这应该是非常简单的计算。我想要一些矩阵实现,因为我有大量的数据,需要保持尽可能低的运行时间。或者,< 浏览1提问于2012-05-15得票数 3 回答已采纳...
This paper focuses on some theoretical properties of the Jensen-Shannon divergence (JSD) that well match human visual system (HVS) features. In particular, it is firstly shown that JSD between the probability density function (pdf) of the reference (original) image and the test (distorted) one...
It can be found that JSD shows better robustness than Kullback-Leibler divergence (KLD) on quantifying time series irreversibility and correctly distinguishes the different type of simulated series. For the empirical analysis, JSD based on HVG is able to detect the significant time irreversibility of...