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A novel method of data masking is presented which allows Poisson and some Gaussian data sets to be altered without changing the overall probability density function. The method consists of multiplying each entry by a random complex exponential function, i.e. a rotation in the complex plane, and exchanging imaginary components between entries. The transformation preserves the properties of the data...
This paper mainly deals with the distributed estimation fusion problem when the correlations are unknown. The local estimates are represented as a set of probability density functions, on which a Riemannian structure endowed with the Fisher metric is built. From the perspective of information geometry, the fused density is formulated as the Fisher barycenter in the space of probability densities and...
This paper considers measuring brain functional connectivity using mutual information (MI). First, we explain the advantage of MI based analysis over the conventional correlation based analysis. Second, we propose a novel approach for MI estimation by exploiting kernel-based probability density function (pdf) estimation and optimization under the maximum likelihood criteria. Finally, the proposed...
Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval, but which also enable the interpretability of these operations by assigning explicit relevance weights to the single data components. Starting with the work SSCI13Stretal, it has been noticed, however, that this...
Gaussian mixture models (GMMs) are commonly used in text-independent speaker verification for modeling the spectral distribution of speech. Recent studies have shown the effectiveness of characterizing speaker information using the mean super-vector obtained by concatenating the mean vectors of the GMM. This paper proposes to use the spatial correlation captured by the covariance matrix of the mean...
In this paper, an adaptive sampling method is proposed for the statistical SRAM cell analysis. The method is composed of two components. One part is the adaptive sampler that manipulates an alternative sampling distribution iteratively to minimize the estimated yield error. The drifts of the sampling distribution are re-configured in each iteration toward further minimization of the estimation variance...
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