| 基于ICA与SVM的孤立点挖掘模型 |
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| 引用本文： | 彭红毅,蒋春福,朱思铭. 基于ICA与SVM的孤立点挖掘模型[J]. 计算机科学, 2006, 33(9): 175-177 |
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| 作者姓名： | 彭红毅 蒋春福 朱思铭 |
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| 作者单位： | 华南农业大学理学院,广州,510642;中山大学数学与计算科学学院,广州,510275 |
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| 基金项目： | 本文得到国家自然科学基金项目（10371135）资助. |
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| 摘 要： | 本文提出一种基于独立成分分析（ICA）与支持向量机（SVM）的孤立点挖掘模型ISOM模型，用ICA对观测到的多维随机向量进行独立成分分解，用SVM估计独立成分的密度函数，克服了传统孤立点挖掘方法的一些缺点，为数据挖掘提供了一种有效的方法，并通过实验验证了该模型的合理性与正确性。
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| 关 键 词： | 孤立点 ICA SVM 密度函数估计 |
Outlier Mining Model Based on ICA & SVM |
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| PENG Hong-Yi,JIANG Chun-Fu,ZHU Si-Ming. Outlier Mining Model Based on ICA & SVM[J]. Computer Science, 2006, 33(9): 175-177 |
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| Authors: | PENG Hong-Yi JIANG Chun-Fu ZHU Si-Ming |
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| Affiliation: | 1. College of Science, South China Agricultural University, Guangzhou 510642 ;2. Department of Mathematics, Sun Yat-sen University, Guangzhou 510275 |
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| Abstract: | ISOM,Outlier Mining Model Based on ICA & SVM,is presented in this paper.This model transforms an observed multidimensional random vector into mutually independent components by ICA and estimates independent components' density function by SVM.Overcoming the defects of traditional outlier mining,the model of ISOM pro- vides an efficient method for data mining,and its correctness and reasonableness ars also validated by the experiment results in this paper. |
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| Keywords: | Outlier ICA(independent component analysis) SVM(Support Vector Machine) Estimation of density function |
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