• Communications for Statistical Applications and Methods
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• 제23권1호
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• pp.71-83
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• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.317-325
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• 2014

Consider a nonlinear transform ${\Phi}(x)$ of x in $\mathbb{R}^p$ to Hilbert space H and assume that the dot product between ${\Phi}(x)$ and ${\Phi}(x^{\prime})$ in H is given by < ${\Phi}(x)$, ${\Phi}(x^{\prime})$ >= K(x, x'). The aim of this paper is to propose a mathematical technique to take screen shots of the multivariate dataset mapped to Hilbert space H, particularly suited to Gaussian kernel $K({\cdot},{\cdot})$, which is defined by $K(x,x^{\prime})={\exp}(-{\sigma}{\parallel}x-x^{\prime}{\parallel}^2)$, ${\sigma}$ > 0. Several numerical examples are given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• 한국정보전자통신기술학회논문지
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• 제2권3호
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• pp.39-52
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• 2009

Scale Invariant Feature Transform (SIFT) is an effective algorithm in object recognition, panorama stitching, and image matching, however, due to its complexity, real time processing is difficult to achieve with software approaches. This paper proposes using a reconfigurable hardware processor with integer half kernel. The integer half kernel Gaussian reduces the Gaussian pyramid complexity in about half [] and the reconfigurable processor carries out a parallel implementation of a full search Fast SIFT algorithm. We use a low memory, fine grain single instruction stream multiple data stream (SIMD) pixel processor that is currently being developed. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and I/O capabilities of the processor which results in a system that can perform real time image and video compression. We apply this novel implementation to images and measure the effectiveness. Experimental simulation results indicate that the proposed implementation is capable of real time applications.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.265-276
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• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.

• Communications for Statistical Applications and Methods
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• 제19권4호
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• pp.517-526
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• 2012

In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.

• 대한음성학회지:말소리
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• 제66호
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.808-810
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• 2011

입력영상에 대하여 전처리과정으로 배경을 분리하는 것이 영상처리 및 인식 성능에 중요한 영향을 준다. 본 논문에서는 화재검출을 위한 영상인식 전처리로 활용하는 다양한 배경추정 알고리즘에 대하여 계산량과 배경추정 성능 분석하였다. 비교하는 배경추정알고리즘은 Gaussian Running Average 추정기법, Mixture of Gaussian 모델, 그리고 KDE (kernel density estimate) 알고리즘에 대한 성능을 평가하였다. 입력영상에 대하여 배경영상차로부터 연기를 검출하는데 있어 KDE 알고리즘이 배경추정 성능은 우수한 것을 확인하였다.

• 한국해안·해양공학회논문집
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• 제28권3호
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• pp.171-176
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• 2016

기온과 수온의 분포형태는 발생빈도의 양상을 결정하는 기본적이고 필수적인 정보이다. 또한 기후변화에 의한 기온과 수온의 장기변화 양상 파악에 유용하다. 기온과 수온의 전형적인 분포형태는 다수의 첨두(mode)를 가지는 형태로 일반적으로 널리 사용되는 정규분포로 표현하기에는 한계가 있다. 본 연구에서는 Gaussian 혼합함수와 Kernel 분포함수를 보다 기온과 수온의 보다 적합한 분포함수 형태로 제안한다. 제안된 분포함수를 우리나라 연안 기온과 수온자료를 이용하여 추정-평가한 결과, 관측 자료의 분포는 꼬리 영역에서 크게 차이를 보이고 있는 것으로 파악되었다. 높은 수온영역과 낮은 기온 영역에서 꼬리 영역이 길게 나타나고 있다. 또한 본 연구에서 제안한 분포함수 추정 및 비교는 기온과 수온의 상호 변동관계 및 장기적인 변동양상을 파악할 수 있다. 그러나 평균 기온 및 수온 그리고 정규분포 함수 형태로는 이러한 변화 양상의 파악은 크게 제한되고 있다.