| 基于CCIPCA和ICA降维的文本分类研究 |
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| 引用本文： | 何海斌,李新福,赵蕾蕾. 基于CCIPCA和ICA降维的文本分类研究[J]. 计算机工程与应用, 2008, 44(29): 150-152. DOI: 10.3778/j.issn.1002-8331.2008.29.042 |
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| 作者姓名： | 何海斌 李新福 赵蕾蕾 |
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| 作者单位： | 河北大学,数学与计算机学院,河北,保定,071002;河北大学,数学与计算机学院,河北,保定,071002;河北大学,数学与计算机学院,河北,保定,071002 |
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| 基金项目： | 国家自然科学基金，河北省自然科学基金，河北省教育厅科研项目 |
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| 摘 要： | 文本分类中采用向量空间模型来表达文本特征，维数巨大，关键是对高维的特征集进行降维处理，而一般的分解算法无法处理大规模的高维问题。采用CCIPCA与ICA相结合的特征提取方法可以有效地实现文本特征降维。实验结果表明降维提高了分类器的效率和效果。
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| 关 键 词： | 文本分类 特征降维 直观无协方差增量主元分析 独立成分分析 支持向量机 |
| 收稿时间： | 2008-04-23 |
| 修稿时间： | 2008-7-21 |
Research of text categorization based on CCIPCA and ICA |
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| HE Hai-bin,LI Xin-fu,ZHAO Lei-lei. Research of text categorization based on CCIPCA and ICA[J]. Computer Engineering and Applications, 2008, 44(29): 150-152. DOI: 10.3778/j.issn.1002-8331.2008.29.042 |
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| Authors: | HE Hai-bin LI Xin-fu ZHAO Lei-lei |
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| Affiliation: | Faculty of Mathematics and Computer Science，Hebei University，Baoding，Hebei 071002，China |
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| Abstract: | Vector Space Model is usually used to express text for text categorization.The process of dimension reduction is a very key problem for practical text categorization.The classical decomposition algorithms are incapable of solving these problems with high-dimensional and large-scale.In this paper an approach to reduce dimensionality of feature space is presented by using candid incremental principal component analysis and independent component analysis algorithm.The experimental result shows that the proposed method for dimension reduction is feasible and effective. |
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| Keywords: | text categorization dimension reduction Candid Covariance-free Incremental Principal Component Analysis(CCIPCA) Independent Components Analysis(ICA) Support Vector Machine(SVM) |
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