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局部保留最大信息差υ-支持向量机

陶剑文 王士同

陶剑文, 王士同. 局部保留最大信息差υ-支持向量机. 自动化学报, 2012, 38(1): 97-108. doi: 10.3724/SP.J.1004.2012.00097
引用本文: 陶剑文, 王士同. 局部保留最大信息差υ-支持向量机. 自动化学报, 2012, 38(1): 97-108. doi: 10.3724/SP.J.1004.2012.00097
TAO Jian-Wen, WANG Shi-Tong. Locality-preserved Maximum Information Variance υ-support Vector Machine. ACTA AUTOMATICA SINICA, 2012, 38(1): 97-108. doi: 10.3724/SP.J.1004.2012.00097
Citation: TAO Jian-Wen, WANG Shi-Tong. Locality-preserved Maximum Information Variance υ-support Vector Machine. ACTA AUTOMATICA SINICA, 2012, 38(1): 97-108. doi: 10.3724/SP.J.1004.2012.00097

局部保留最大信息差υ-支持向量机

doi: 10.3724/SP.J.1004.2012.00097
详细信息
    通讯作者:

    王士同 江南大学信息工程学院教授. 主要研究方向为人工智能,机器学习.本文通信作者.E-mail: wxwangst@yahoo.com.cn

Locality-preserved Maximum Information Variance υ-support Vector Machine

  • 摘要: 针对现有模式分类方法不能较好地保持数据空间的局部流形信息或差异信息等问题,提出一种基于流形学习的局部保留最大信息差υ-支持向量机(Locality-preserved maximum information variance υ-support vector machine,υ-LPMIVSVM).对于模式分类问题,v-LPMIVSVM引入局部同类离散度和局部异类离散度概念,分别体现输入空间局部流形结构和局部差异(或判别)信息,通过最小化局部同类离散度和最大化局部异类离散度,优化分类器的投影方向.同时,υ-LPMIVSVM采用适于流形数据的测地线距离来度量数据点对间的相似性,以更好地反映流形数据的本质结构.人造和实际数据集实验结果显示所提方法具有良好的泛化性能.
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  • 收稿日期:  2010-12-08
  • 修回日期:  2011-07-01
  • 刊出日期:  2012-01-20

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