• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.361-368
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• 2004

The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.243-248
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• 2003

An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.86-88
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• 2010

본 논문에서는 회귀문제를 위한 비선형 특징 추출방법을 제안하고 분류문제에 적용한다. 이 방법은 이미 제안된 선형판별 분석법을 회귀문제에 적용한 회귀선형판별분석법(Linear Discriminant Analysis for regression:LDAr)을 비선형 문제에 대해 확장한 것이다. 본 논문에서는 이를 위해 커널함수를 이용하여 비선형 문제로 확장하였다. 기본적인 아이디어는 입력 특징 공간을 커널 함수를 이용하여 새로운 고차원의 특징 공간으로 확장을 한 후, 샘플 간의 거리가 큰 것과 작은 것의 비율을 최대화하는 것이다. 일반적으로 얼굴 인식과 같은 응용 분야에서 얼굴의 크기, 회전과 같은 것들은 회귀문제에 있어서 비선형적이며 복잡한 문제로 인식되고 있다. 본 논문에서는 회귀 문제에 대한 간단한 실험을 수행하였으며 회귀선형판별분석법(LDAr)을 이용한 결과보다 향상된 결과를 얻을 수 있었다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.11-18
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• 2010

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.454-459
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• 2012

A kernel function of apparent earth resistivity can be estimated using the apparent earth resistivity measured with Wenner's 4 point method. It becomes to solve a nonlinear system to estimate the kernel function of apparent earth resistivity. However it is not simple to get solution of nonlinear system with many unknown variables. This paper suggests the method of estimating kernel function by linearizing this nonlinear system. Finally, various examples of earth structure have been simulated to evaluate the proposed method in this paper.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.714-717
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• 2011

본 논문에서는 선체 블록의 운반 작업 중 발생하는 동적 하중 및 골리앗 크레인의 와이어로프와 선체블록 간의 동적 접촉력을 고려한 최적 러그 배치 시스템을 설계하고, 다물체계 동역학 커널과 외력 계산커널을 개발하였다. 다물체계 동역학 커널은 recursive formulation을 이용하여 운동 방정식을 구성하고, 외력 계산 커널은 비선형 유체정역학적 힘, 선형 유체동역학적 힘, 풍력, 계류력을 계산할 수 있다. 이를 이용해 블록에 작용하는 와이어로프와 블록 간의 간섭과 동적 접촉력을 계산하고, 그 결과를 이용하여 러그가 부착된 블록의 구조 해석을 수행하였다.

• Journal of Korean Library and Information Science Society
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• v.48 no.2
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• pp.215-236
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• 2017

The purpose of this study is to develop an automatic classification system for recommending appropriate books of 9 enneagram personality types, using book information data reviewed by librarians. Data used for this study are book review of 501 recommended titles for children and young adults from National Library for Children and Young Adults. This study is implemented on the assumption that most people prefer different types of books, depending on their preference or personality type. Performance test for two different types of machine learning models, nonlinear kernel and linear kernel, composed of 360 clustering models with 6 different types of index term weighting and feature selections, and 10 feature selection critical mass were experimented. It is appeared that LIBLINEAR has better performance than that of LibSVM(RBF kernel). Although the performance of the developed system in this study is relatively below expectations, and the high level of difficulty in personality type base classification take into consideration, it is meaningful as a result of early stage of the experiment.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.95-103
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• 1998

Azzalini and Bowman proposed the pseudo-likelihood ratio test for checking the linear relationship using kernel regression estimator when the error of the regression model follows the normal distribution. We modify their method with the bootstrap technique to construct a new test, and examine the power of our test through simulation. Our method can be applied to the case where the distribution of the error is not normal.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.177-181
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• 2005

기계학습에서 커널을 이용한 방법은 그 응용범위가 기계학습의 전반에 걸쳐 다양하게 이용되고 있으며, 그 성능 또한 기존의 방법들을 앞지르고 있다. 이는 기존의 비선형적 접근을 커널을 이용한 고차원 공간에서의 선형적 접근법으로 바꿈으로써 가능하게 되는 것이다. 다양한 분야에 적용되는 많은 커널들이 존재하며 각 커널들은 특별한 분야에 적용되기 쉽도록 다른 형태를 띠고 있기도 하지만, 커널로서 작용하기 위해 양한정 조건(positive definiteness)을 만족해야 한다. 본 연구에서는 DNA 문제에 직접 적용시킬 수 있는 방법으로서의 새로운 커널을 제시한다. 또한 매트로폴리스(Metropolis) 알고리즘을 이용하여 DNA의 hybridization과정을 모사함으로써 새로운 종류의 커널이 양한정(positive definite) 조건을 만족시킬 수 있는 방법을 제시한다. 새로 만들어진 커널이 행렬값을 형성해 나가는 과정을 살펴보면 인간이 예(instance)로부터 개념을 형성해 나가는 과정과 흡사한 양상을 보이는 것을 알 수 있다. 개념을 나타내는 좋은 예로서의 표본(prototype)으로부터 개념이 형성되어 가는 과정은 표본(prototype)이 아닌 예로부터 개념이 형성되는 과정과 다른 양상을 띠는 것과 같은 모양을 보인다.