高维空间多分辨率最小生成树模型的自适应一类分类算法

胡正平; 冯凯

doi:10.3724/SP.J.1004.2012.00769

高维空间多分辨率最小生成树模型的自适应一类分类算法

doi: 10.3724/SP.J.1004.2012.00769 cstr: 32138.14.SP.J.1004.2012.00769

胡正平,
冯凯

1.
燕山大学信息科学与工程学院秦皇岛 066000

详细信息

通讯作者:
胡正平, 燕山大学博士, 教授. 主要研究方向为统计学习理论, 模式识别, 医学图像处理.

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- 文章访问数: 2182
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- 被引次数: 0
出版历程
- 收稿日期: 2011-05-30
- 修回日期: 2011-08-30
- 刊出日期: 2012-05-20

An Adaptive One-class Classification Algorithm Based on Multi-resolution Minimum Spanning Tree Model in High-dimensional Space

1.
Institute of Information Science and Engineering, Yanshan University, Qinhuangdao 066000

摘要

摘要: 基于最小生成树数据描述(Minimum spanning tree class descriptor, MSTCD)法覆盖半径固定不变,难以形成局部结构紧致性描述. 本文将多尺度分析思想和最小生成树(Minimum spanning tree, MST)结构相结合,提出最小生成树的自适应多分辨率覆盖模型. 该模型利用样本流形的自身结构特点实现对数据的多分辨分析, 任意位置的分辨率由对应的''点''结构和''边''结构共同决定,整体覆盖模型拥有多个覆盖半径, 数据当前位置不同、分辨率不同,实现在多分辨尺度下对数据流形的自适应紧致覆盖. 实验结果表明该方法具有一定的合理性.
- 一类分类 /
- 最小生成树 /
- 多分辨分析 /
- 自适应覆盖
Abstract: The coverage radii in the algorithm of MSTCD (Minimum spanning tree class descriptor) are generally fixed without diversification, which makes it difficult to construct a close coverage for the local structure. This article combines multi-resolution thought and minimum spanning tree (MST) covering model, and proposes an adaptive multi-resolution covering model based MST in high-dimensional space. In this algorithm, multi-resolution of data is determined by the distributed feature of data manifold itself. The resolution of any location is depended on the structure of sample point and edge. The proposed novel algorithm permits that the whole covering model could have different coverage radii or resolutions and the resolution has relationship with location. Experiments show that the algorithm is reasonable.
- One-class classification /
- minimum spanning tree (MST) /
- multi-resolution analysis /
- adaptive coverage

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