.png){kind=icon}
返回
Cherry-picking for complex data: robust structure discovery
.png){kind=icon}
PRE
AI
DOI：10.1098/rsta.2009.0119.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Complex data often arise as a superposition of data generated from several simpler models. The traditional strategy for such cases is to use mixture modelling, but it can be problematic, especially in higher dimensions. This paper considers an alternative approach, emphasizing data exploration and robustness to model misspecification. The strategy is applied to problems in regression, cluster analysis and multidimensional scaling. The approach is illustrated through simulation and the analysis of several datasets.
Keyword:
cluster analysis
elemental sets
mixture models
multidimensional scaling
regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
3.7
论文数:
7.8K
被引数:
2.8W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容