Pca built-in function and how its works?

Can anyone tell me the pca built-in function for machine learning also which one dataset are used for dimensionality reduction... Thnx in advance

Answer 1

[KSSV]

REad abut Singula Value DEcomposition. svd . And refer this for more clarity:

http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

[Muhammad Ibrar]

Thnx But What Type Of Datasets We Wll Use For Pca...

[KSSV]

Matrices...a 2D matrix. Check the documentation..you got many examples: https://in.mathworks.com/help/stats/pca.html

[Muhammad Ibrar]

Ok thnx let me check if I got a problem I'll contct u...

Answer 2

[Steven Lord]

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.

[Muhammad Ibrar]

Actually i want to know that what type of dataset we'll use in pca... If u have a dataset that will use in pca... Kindly tell me

[Steven Lord]

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]

Can u send some example of 1 dataset plz actually im new to implement this