Self-Organising Map (SOM) with Principle Component Analysis (PCA)

4 次查看（过去 30 天）
naghmeh moradpoor
naghmeh moradpoor 2017-6-19
回答： Greg Heath 2017-6-22
Dear all, I want to use Self-Organising Map (SOM) [unsupervised machine learning] for my anomaly detection problem. But before that I would like to find suitable input features that cause the best results. I have total of eight input features. Would you use Principle Component Analysis (PCA) to find best features? What would you do? Regards, Naghmeh

请先登录，再回答此问题。

采纳的回答

Greg Heath
Greg Heath 2017-6-22
It is not clear if you have a well defined output.
If so, it IS NOT the variation of the inputs that are paramount.
It IS the variation of the outputs w.r.t. the inputs.
Check out principal COORDINATE analysis (very different from principal COMPONENT analysis!)
Hope that helps.
Thank you for formally accepting my answer
Greg

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Dimensionality Reduction and Feature Extraction 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by