• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Sixth International Conference on Quality Control by Artificial Vision May 19-22, 2003 Gatlinburg, Tennessee, USA >Comparison of Dimensionality Reduction Methods for Wood Surface Inspection
【24h】

Comparison of Dimensionality Reduction Methods for Wood Surface Inspection

机译：木材表面检查的降维方法比较

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Dimensionality reduction methods for visualization map the original high-dimensional data typically into two dimensions. Mapping preserves the important information of the data, and in order to be useful, fulfils the needs of a human observer. We have proposed a self-organizing map (SOM)- based approach for visual surface inspection. The method provides the advantages of unsupervised learning and an intuitive user interface that allows one to very easily set and tune the class boundaries based on observations made on visualization, for example, to adapt to changing conditions or material. There are, however, some problems with a SOM. It does not address the true distances between data, and it has a tendency to ignore rare samples in the training set at the expense of more accurate representation of common samples. In this paper, some alternative methods for a SOM are evaluated. These methods, PCA, MDS, LLE, ISOMAP, and GTM, are used to reduce dimensionality in order to visualize the data. Their principal differences are discussed and performances quantitatively evaluated in a few special classification cases, such as in wood inspection using centile features. For the test material experimented with, SOM and GTM outperform the others when classification performance is considered. For data mining kinds of applications, ISOMAP and LLE appear to be more promising methods.
机译：用于可视化的降维方法通常将原始高维数据映射为二维。映射保留了数据的重要信息，并且为了有用，满足了人类观察者的需求。我们提出了一种基于自组织图（SOM）的视觉表面检查方法。该方法的优点是无监督学习和直观的用户界面，该界面允许用户根据可视化观察非常轻松地设置和调整班级边界，例如以适应变化的条件或材料。但是，SOM存在一些问题。它没有解决数据之间的真实距离，并且倾向于忽略训练集中的稀有样本，但会以更准确地表示普通样本为代价。在本文中，评估了SOM的一些替代方法。这些方法PCA，MDS，LLE，ISOMAP和GTM用于降低维数以可视化数据。讨论了它们的主要区别，并在一些特殊分类情况下（例如，在使用百分位数特征的木材检查中）对性能进行了定量评估。对于试验的测试材料，在考虑分类性能时，SOM和GTM优于其他材料。对于数据挖掘这类应用程序，ISOMAP和LLE似乎是更有希望的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号