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基于独立成分分析的分解向前SVM降维算法 被引量：2

Decomposition forward SVM dimension-reduction algorithm based on independent component analysis
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摘要 提出一种基于大样本学习的分解向前支持向量机算法和一种新的基于独立成分分析的降维学习模型,其算法的复杂度比传统块算法和标准SVM低。利用不完备ICA思想,达到数据压缩而降维的目的。实验发现,由于降低了输入维数,简化了数据结构,从而减少了SVM识别的计算复杂度,当把向量维数从110维降低到5维时,平均识别率超过传统神经网络达到93%,因而从计算时间和识别效率二者的综合情况来考虑,ICA降维模型是一种理想的实际应用模型。 A Decomposition Forward Support Vector Machine （DFSVM） algorithm for large scale samples learning and a new dimension reduction model based on Independent Component Analysis （ICA） were proposed. The calculational complexity is lower than that of the traditional chunking algorithm and the standard SVM. Use the idea of imcomplete ICA to compress data and reduce the dimension. Because of the reduced input dimension and simplified data structure, the calculational complexity of SVM has been reduced. Experiment indicates that if reducing the dimension from one hundred and ten dimension to five-dimension, the average recognition rate is superior to traditional neural network and comes to 93%. Considering the time cost and the recognition efficiency, ICA dimension reduction model is an ideal application model in practice.
作者 罗泽举 宋丽红 朱思铭
机构地区 重庆工商大学计算机科学与信息工程学院 重庆工商大学实验实习中心 中山大学数学与计算科学学院
出处 《计算机应用》 CSCD 北大核心 2007年第9期2249-2252,共4页 journal of Computer Applications
基金 重庆市教育委员会科学技术研究项目(KJ0707022)
关键词 独立成分分析 分解向前支持向量机 蛋白质序列识别 Independent component analysis （ICA） Decomposition Forward Support Vector Machine （DFSVM） recognition for protein sequence
分类号 TP391.4 [自动化与计算机技术—计算机应用技术] TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

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共引文献85

同被引文献21

  • 1刘隽,周涛,周佩玲.GA优化支持向量机用于混沌时间序列预测[J].中国科学技术大学学报,2005,35(2):258-263. 被引量：21
  • 2张建明,林亚平,吴宏斌,杨格兰.独立成分分析的研究进展[J].系统仿真学报,2006,18(4):992-997. 被引量：31
  • 3何中市,刘里.基于上下文关系的文本分类特征描述方法[J].计算机科学,2007,34(5):183-186. 被引量：6
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引证文献2

二级引证文献4

计算机应用
2007年 第9期

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