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高维数据特征降维研究综述 被引量：65

Survey on feature dimension reduction for high-dimensional data
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摘要 特征降维能够有效地提高机器学习的效率,特征子集的搜索过程以及特征评价标准是特征降维的两个核心问题。综述国际上关于特征降维的研究成果,总结并提出了较完备的特征降维模型定义;通过列举解决特征降维上重要问题的各种方案来比较各种算法的特点以及优劣,并讨论了该方向上尚未解决的问题和发展趋势。 Feature dimension reduction is effective in improving machine learning, the point is how to search the subset and selection criteria. This paper defined general models for dimension reduction, compared different approaches, and discussed the unresolved topics and development trends.
作者 胡洁
机构地区 北京大学视觉与听觉信息处理实验室
出处 《计算机应用研究》 CSCD 北大核心 2008年第9期2601-2606,共6页 Application Research of Computers
关键词 降维 机器学习 特征选择 特征抽取 评估准则 dimension reduction machine learning feature selection feature abstraction selection criteria
分类号 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36

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同被引文献533

引证文献65

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计算机应用研究
2008年 第9期

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