Pca built-in function and how its works?

4 次查看（过去 30 天）
Muhammad Ibrar
Muhammad Ibrar 2019-5-24
评论： Muhammad Ibrar 2019-5-24
Can anyone tell me the pca built-in function for machine learning also which one dataset are used for dimensionality reduction... Thnx in advance

请先登录，再回答此问题。

采纳的回答

KSSV
KSSV 2019-5-24
REad abut Singula Value DEcomposition. svd . And refer this for more clarity:
http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

更多回答（1 个）

Steven Lord
Steven Lord 2019-5-24
The books and papers listed in the References section on the documentation page for the pca function in Statistics and Machine Learning Toolbox may be of interest if you want to know the technical details behind principal component analysis. The page linked as the second entry in the Topics section of that page gives a brief overview of what PCA is.
  3 个评论
显示 1更早的评论
Steven Lord
Steven Lord 2019-5-24
The main point behind PCA is that you use it to analyze your data to identify your data's principal components and learn more about your data.
If you want a sample dataset to experiment with pca you could use rand, randn, randi, gallery, ones, zeros, eye, etc. Some of those would make for more interesting experiments than others.
Muhammad Ibrar
Muhammad Ibrar 2019-5-24
Can u send some example of 1 dataset plz actually im new to implement this

请先登录，再进行评论。

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