## Summary
[![View Detect outliers in multivaraite datasets on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/65817-detect-outliers-in-multivaraite-datasets)
This submission contains Matlab implementation of an iterative **multivariate outlier detection algorithm**
described in [Hadi (1992)] [[1]]. In addition to flagging potential outliers, the main function
`DetectMultVarOutliers.m` also outputs robust estimates of the mean and covariance that it computes
during execution.
Deviating slightly from [Hadi (1992)], `DetectMultVarOutliers.m` initializes the sample mean with the [geometric median]
of the dataset, instead of the coordinate-wise median. `GeometricMedian.m` is the function used compute this
robust statistic; via the Weiszfeld's algorithm [[2]]. Note that this auxiliary function can be used on its own in
any application that requires robust estimation of central tendency of multivariate data corrupted by sampling
errors and/or noise.
For a quick demo on how to use the main function, see source code for `outliers_demo.m` or simply enter `outliers_demo`
into Matlab command window.
## References
[**[1]**] Hadi, A.S., 1992. Identifying multiple outliers in multivariate data. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 54(3), pp. 761-771.
[**[2]**] Weiszfeld, E., 1937. Sur le point par lequel la somme des distances den points donnés est minimum. Tohoku Mathematics Journal, Vol. 43, pp. 355–386.
## License
[MIT] © 2019 Anton Semechko
a.semechko@gmail.com
[Hadi (1992)]: https://www.researchgate.net/profile/Ali_Hadi/publication/243777821_Identifying_Multiple_Outliers_in_Multivariate_Data/links/5406dda50cf2c48563b2732e.pdf
[1]: https://www.researchgate.net/profile/Ali_Hadi/publication/243777821_Identifying_Multiple_Outliers_in_Multivariate_Data/links/5406dda50cf2c48563b2732e.pdf
[geometric median]: http://en.wikipedia.org/wiki/Geometric_median
[2]: http://en.wikipedia.org/wiki/Geometric_median
[source code]: https://github.com/AntonSemechko/Multivariate-Outliers/blob/master/outliers_demo.m
[MIT]: https://github.com/AntonSemechko/Multivariate-Outliers/blob/master/LICENSE.md
【数据分析】基于Matlab检测多元数据集中的异常值.zip
版权申诉
62 浏览量
2023-04-20
09:51:01
上传
评论 1
收藏 58KB ZIP 举报
Matlab科研辅导帮
- 粉丝: 1w+
- 资源: 7483
最新资源
- Assignment2(4).ipynb
- 用pytorch框架实现的油井时间序列动态预测的模型,其中包含一些传统的时间序列预测方法 .zip
- TimesNet作为一般时间序列分析强大的基础模型 在长短期预测、插补、异常检测和分类5个主流任务上取得了一致的前沿成果.zip
- 实现结构体序列化和反序列化工具类CSearchive,支持基本类型,C++STL容器以及对象 .zip
- 时间序列遥感变化检测.zip
- 时间序列数据集收集以及数据分析.zip
- 时间序列分析ARIMA预测模型.zip
- 深度学习- 时间序列预测.zip
- 计算给定时间序列的平均值、方差、概率分布(大致的分布)、自协方差函数.zip
- 基于spring boot上的注解缓存,自带轻量级缓存管理页面.zip
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈