• Title/Summary/Keyword: fuzzy metric space

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A Note on the Defuzzification Method and Distance Metric of Fuzzy Color Model (퍼지 컬러 모델의 비퍼지화 방법과 거리 척도의 제안)

  • Kim, Dae-Won;Lee, Kwang H.
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.10b
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    • pp.40-42
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    • 2001
  • Most people have to deal with color and color problems occasionally. There are many strange things about color and color vision that most people do not notice. Even though color seems intuitive and simple it is not. In this paper, we modeled the color using fuzzy set theory. The proposed fuzzy color model is based on the Munsell color space. We defined several fuzzy color terminologies, and proposed a extended center of gravity defuzzification mthod for fuzzy color set. Finally, three distance measures between fuzzy colors were also formulated.

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Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA

  • Lee, Hansung;Yoo, Jang-Hee;Park, Daihee
    • ETRI Journal
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    • v.36 no.3
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    • pp.333-342
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    • 2014
  • Most hyper-ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex-shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K-means algorithm, fuzzy C-means algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance.

A Study on the Optimal Mahalanobis Distance for Speech Recognition

  • Lee, Chang-Young
    • Speech Sciences
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    • v.13 no.4
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    • pp.177-186
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    • 2006
  • In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

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On comonotonically additive interval-valued functionals and interval-valued Choquet integrals(II) (보단조 가법 구간치 범함수와 구간치 쇼케이적분에 관한 연구(II))

  • Jang, Lee-Chae;Kim, Tae-Kyun;Jeon, Jong-Duek
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.1
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    • pp.33-38
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    • 2004
  • In this paper, we will define comonotonically additive interval-valued functionals which are generalized comonotonically additive real-valued functionals in Schmeidler[14] and Narukawa[12], and prove some properties of them. And we also investigate some relations between comonotonically additive interval-valued functionals and interval-valued Choquet integrals on a suitable function space, cf.[9,10,11,13].