探索海洋传感器数据的与中心度相关的属性Exploring the hubness-related properties of oceanographic sensor data |
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课程网址: | http://videolectures.net/sikdd2011_tomasev_oceanographic/ |
主讲教师: | Nenad Tomašev |
开课单位: | 约瑟夫·斯特凡学院 |
开课时间: | 2011-11-04 |
课程语种: | 英语 |
中文简介: | 在本文中,我们研究了海洋传感器数据的高维度如何影响最近邻机器学习方法的潜在使用。我们专注于维数诅咒的一个特殊后果–中枢。我们检查了海洋学数据的中心性,并展示了如何将其用于可视化和检测原型传感器/位置以及模棱两可和潜在错误的传感器/位置。我们继续在数据上定义一个简单的分类问题,表明最近开发的具有中心度意识的分类方法可能有助于克服传感器数据中的一些与中心度有关的问题。 p> |
课程简介: | In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning methods. We focus on one particular consequence of the curse of dimensionality – hubness. We examine the hubness of oceanographic data and show how it can be used to visualize and detect both prototypical sensors/locations, as well as ambiguous and potentially erroneous ones. We proceed to define an easy classification problem on the data, showing that the recently developed hubness-aware classification methods may help to overcome some of the hubness-related issues in sensor data. |
关 键 词: | 海洋传感器; 维数诅咒; 中心度 |
课程来源: | 视频讲座网 |
数据采集: | 2021-01-15:yumf |
最后编审: | 2021-01-15:yumf |
阅读次数: | 16 |