{"title":"Erratum to: Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal","authors":"Xian Zang, IV FelipeP.Vista, K. Chong","doi":"10.1631/jzus.C13e0320","DOIUrl":null,"url":null,"abstract":"We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F (FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F (KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means (FCM): sensitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the computational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.","PeriodicalId":49947,"journal":{"name":"Journal of Zhejiang University-Science C-Computers & Electronics","volume":"15 1","pages":"1086"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1631/jzus.C13e0320","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Zhejiang University-Science C-Computers & Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1631/jzus.C13e0320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F (FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F (KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means (FCM): sensitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the computational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.
本文提出了一种基于快速全局核模糊c-means-F (FGKFCM-F)的聚类算法,其中F为核化特征空间。该算法使用基于核的模糊c-均值- f (KFCM-F)作为局部搜索过程,通过求解所有中间问题,以增量方式推导出近最优解。该算法由于继承了KFCM-F算法的增量特性和非线性特性,克服了模糊c-均值算法对初始化的敏感性和不能使用非线性可分数据的缺点。为了在不显著影响求解质量的前提下降低计算复杂度,提出了一种加速方案。在一个非线性人工数据集和一个真实语音信号数据集上进行了实验,以测试所提出的算法用于辅音/元音分割。仿真结果证明了该算法在两种类型数据集上提高聚类性能的有效性。