Results 181 to 190 of about 10,729 (204)

Kullback–Leibler divergence for evaluating bioequivalence

Statistics in Medicine, 2003
AbstractIn this paper we propose a methodology for evaluating the bioequivalence of two formulations of a drug that encompasses not only average bioequivalence (ABE), but also the more recently introduced measures of population bioequivalence (PBE) and individual bioequivalence (IBE). The latter two measures are concerned with prescribability (PBE) and
Vladimir, Dragalin, Valerii, Fedorov, Scott, Patterson, Byron, Jones   +3 more
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Acoustic environment identification by Kullback–Leibler divergence

Forensic Science International, 2017
This paper presents a forensic methodology that determines, from among a set of recording places, the probable place where allegedly a disputed digital audio recording was made. The methodology considers that digital audio recordings are noisy signals that have two involved noise components.
G, Delgado-Gutiérrez, F, Rodríguez-Santos, O, Jiménez-Ramírez, R, Vázquez-Medina   +3 more
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Source Resolvability with Kullback-Leibler Divergence

2018 IEEE International Symposium on Information Theory (ISIT), 2018
The first- and second-order optimum achievable rates in the source resolvability problem are considered for general sources. In the literature, the achievable rates in the resolvability problem with respect to the variational distance as well as the normalized Kullback-Leibler (KL) divergence have already been analyzed. On the other hand, in this study
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Complex NMF with the generalized Kullback-Leibler divergence

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
We previously introduced a phase-aware variant of the non-negative matrix factorization (NMF) approach for audio source separation, which we call the “Complex NMF (CNMF).” This approach makes it possible to realize NMF-like signal decompositions in the complex time-frequency domain. One limitation of the CNMF framework is that the divergence measure is
Hirokazu Kameoka, Hideaki Kagami, Masahiro Yukawa   +2 more
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Estimation of Kullback–Leibler Divergence by Local Likelihood

Annals of the Institute of Statistical Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Young Kyung, Park, Byeong U.
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Matrix CFAR detectors based on symmetrized Kullback–Leibler and total Kullback–Leibler divergences

Digital Signal Processing, 2017
Target detection in clutter is a fundamental problem in radar signal processing. When the received radar signal contains only few pulses, it is difficult to achieve a satisfactory performance using the traditional detection algorithm. In recent times, a generalized constant false alarm rate (CFAR) detector on the Riemannian manifold of Hermitian ...
Xiaoqiang Hua, Yongqiang Cheng 0002, Hongqiang Wang 0001, Yuliang Qin, Yubo Li, Wenpeng Zhang 0002   +5 more
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Estimating the Kullback–Leibler Divergence

2014
We now investigate how the KLD rate can be estimated from a single empirical stationary trajectory, obtained from a stochastic stationary process whose dynamics is unknown. We assume that the empirical stationary trajectory contains \(n\) data of one or several random variables denoted by the letter \(X\).
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Kullback-Leibler Divergence for Nonnegative Matrix Factorization

2011
The I-divergence or unnormalized generalization of Kullback-Leibler (KL) divergence is commonly used in Nonnegative Matrix Factorization (NMF). This divergence has the drawback that its gradients with respect to the factorizing matrices depend heavily on the scales of the matrices, and learning the scales in gradient-descent optimization may require ...
Zhirong Yang, He Zhang 0009, Zhijian Yuan, Erkki Oja   +3 more
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Dissipation and Kullback–Leibler Divergence

2014
In this chapter, we introduce the theoretical framework of the first part of our work, in which we study of the relationship between dissipation and irreversibility quantitatively in microscopic systems in the stationary state.
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Kullback-Leibler divergence estimation of continuous distributions

2008 IEEE International Symposium on Information Theory, 2008
We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour ...
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