CAJ | 학술논문

高维数据中进行各种处理时所需样本数量会成指数级增加，同时样本间距离的价值也逐渐减小，将导致维数灾问题。文本标签数据通常会面临数据维数过高的问题，会影响用户对垃圾标签的检测。文中借助支持向量机的数学模型构建出针对Folksonomy的大规模垃圾标签检测模型。为了减少检测垃圾标签时维数过高的影响，在核主成分分析理论的启发下，将数据降维思想引入数据约简领域，提出基于核主成分分析法的大规模SVM数据集约简模型。最终实例化形成一种新的垃圾标签检测方法，即基于核主成分分析支持向量机( KPCA-SVM)的大规模垃圾标签检测模型。该模型在垃圾标签检测中可以在不影响数据特征的前提下，缩短模型的测试时间且检测性能良好。
고유수거중진행각충처리시소수양본수량회성지수급증가，동시양본간거리적개치야축점감소，장도치유수재문제。문본표첨수거통상회면림수거유수과고적문제，회영향용호대랄급표첨적검측。문중차조지지향량궤적수학모형구건출침대Folksonomy적대규모랄급표첨검측모형。위료감소검측랄급표첨시유수과고적영향，재핵주성분분석이론적계발하，장수거강유사상인입수거약간영역，제출기우핵주성분분석법적대규모SVM수거집약간모형。최종실례화형성일충신적랄급표첨검측방법，즉기우핵주성분분석지지향량궤( KPCA-SVM)적대규모랄급표첨검측모형。해모형재랄급표첨검측중가이재불영향수거특정적전제하，축단모형적측시시간차검측성능량호。
The needed sample will increase exponentially when processing high-dimensional data,the value of the distance between the sample also gradually reduced at the same time,which will lead to the dimension disaster problem. Text label data usually face this prob-lem of high-dimensional data,it will affect the users to detect social spam. In this paper,take advantage of the mathematical model of Support Vector Machine ( SVM) to construct the large-scale social spam detection model for Foklsonomy. In order to reduce the influ-ence of high-dimensional data,inspired by the kernel principal component analysis theory,the ideas of data dimension reduction are intro-duced,the large-scale SVM data set reduction model is proposed which is based on kernel principal component analysis. Finally form a new social spam detection method,the large-scale social spam detection model based on kernel principal component analysis and support vector machine. This model would not affect the characteristics in the social spam detection,and it will shorten the test time and have a good detection performance.