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A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the...
Despite the extensive literature, describing the probability content of measurements remains an important topic for engineering problems. The histogram remains the golden standard, even though kernel density estimation is a strong competitor when smooth estimates are desired.
This article proposes to monitor industrial process faults using Kullback Leibler (KL) divergence. The main idea is to measure the difference between the distributions of normal and faulty data. Sensitivity analysis on the KL divergence under Gaussian distribution assumption is performed, which shows that the sensitivity of KL divergence increases with the number of samples. For non-Gaussian data,...
The paper considers the problem of reconstructing a probability density function from a finite set of samples independently drawn from it.We cast the problem in a Bayesian setting where the unknown density is modeled via a nonlinear transformation of a Bayesian prior placed on a Reproducing Kernel Hilbert Space. The learning of the unknown density function is then formulated as a minimum variance...
This paper proposes a new procedure for calculating high-accuracy PDF estimates, which are free of random error and nearly free of bias error. The procedure is verified experimentally using random jitter and a 16-bitADC.
We propose in this paper an unsupervised Bayesian image segmentation based on non-parametric expectation-maximization (EM) algorithm. The non-parametric aspect comes from the use of the B-spline probability density function (pdf) estimation, which is reduced to the estimation of the parameters of B-splines of the pdf.
In this paper we study the problem of score normalization in biometric verification systems. Specifically, we introduce a new class of normalization techniques, which unlike the commonly used parametric score normalization techniques, such as z- or t-norm, make no assumptions regarding the shape of the underlying score distribution. The proposed class of normalization techniques first estimates the...
This paper suggests the improvement and generalization of the wideband ambiguity function for the case of MIMO radar signals. The proposed generalization of the ambiguity function is based on the multivariate copula notion. As a result it does not depend on a probability density function. This is an important advantage because sounding waveforms and reflected radar signals have different and unknown...
This paper aims to provide decision support for selecting software and hardware architecture for content-based document comparison. We evaluate Java, C, CUDA C and OpenCL implementations of an image characterization algorithm used for content-based document comparison on a CPU and NVIDIA and AMD graphics processing units (GPUs). Based on our experimental results, we conclude that the original Java...
In this paper we suggest the improvement and generalization of the wideband ambiguity function for radar signals. Sounding waveforms and reflected radar signals have different probability density functions, and this fact is not taken into account in classical definition of an ordinary ambiguity function. The proposed generalization of the ambiguity function is based on the copula notion and does not...
The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple...
Clustering is one of the most useful methods for understanding similarity among data. However, most conventional clustering methods do not pay sufficient attention to the geometric properties of data. Geometric algebra (GA) is a generalization of complex numbers and quaternions able to describe spatial objects and the relations between them. This paper uses conformal GA (CGA), which is a part of GA,...
In recent years, hyperspectral Anomaly Detection (AD) has become a challenging area due to the rich information content provided by hyperspectral sensors about the spectral characteristics of the observed materials. Within this framework, since no prior knowledge about the target is assumed, pixels that have different spectral content from typical background pixels are identified as spectral anomalies...
In this paper, a new method for evaluating the quality of clustering of genes is proposed based on mutual information criterion. Instead of using the conventional histogram-based modeling method to assess clustering performance, we derive a normalized mutual information criterion utilizing the Gaussian kernel density estimator. In the computation of the mutual information, we propose to use only cluster-centroids...
We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the pdf. Proposed manifold score can be used to check if data pairs lie on the same manifold. We use this score to i) cluster nonlinear manifolds having irregular shapes, and ii) (down)sample a selected principal curve with sufficient...
Nakagami imaging is an ultrasonic tissue characterization technique which has shown promise for differentiating different breast masses. Two parameters describing the shape and the scale of the distribution of the RF-data envelope are computed on sliding windows. The choice of the size of the windows is critical as both oversized and undersized windows can lead to an unstable estimation of the parameters...
Algorithm parameters' influence on performance of ACOR (extension of ant colony optimization) is analyzed in this paper. Parameter establishment in ACOR is a multi-factor and multi-level optimization problem. And uniform design is introduced for solutions of high quantity to this problem through fewer experiments. This method is proved to be feasible and valid by simulation analysis in this paper.
Many tracking algorithms have difficulties dealing with occlusions and background clutters, and consequently don't converge to an appropriate solution. Tracking based on the mean shift algorithm has shown robust performance in many circumstances but still fails e.g. when encountering dramatic intensity or colour changes in a pre-defined neighbour hood. In this paper, we present a robust tracking algorithm...
In this paper, we propose a new adaptive technique for blind equalisation for fast time-varying channels. The proposed approach is based on fitting the probability density function (pdf) of the equalizer output to the desired pdf of the corresponding symbol alphabet. The underlying pdf at the equalizer output is estimated by means of the Parzen Window method. The cost function of the proposed technique...
A life detection method based on Correntropy Spectral Density (CSD) is proposed. Because correntropy contains second and higher-order moments of the probability density function due to the nonlinearty of the kernel, it is capable of yielding higher output Signal to Noise Ratio (SNR) in non-Gaussian background when compared with traditional life detection algorithm based on the Power Spectral Density...
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