Principal Component Analysis

Different parts of a vehicle

What is PCA?

Principal component algorithm finding the direction of maximum variance

Statistical interpretation


x¯ = [1,1,1,1] * [x¯]

M = x¯ — x¯

M = X (data matrix ) — X (mean of the rows)

K = M(Transpose) *M

therefore K = FG.

T = M * F

In a nutshell, this whole process explained above is meant to decompose a matrix in the direction of maximum variance in order to capture the most important features of a given data matrix.




Uncloaking the mystery in AI

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

What is Bias-Variance Tradeoff?

Why Keeping a Data Team is Harder Than Hiring for It

An island

Analytics — 5 mistakes that companies make

COVID-19: A Contrast Between Hong Kong and Ontario Canada

Stock Price Prediction Using Python & Machine Learning

What Is Big Data Analytics and How Useful Is It to Your Business?

The toolkit for the modern data ninja

Data Science Vs Data Analytics

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Machine Learning Brainweights

Machine Learning Brainweights

Uncloaking the mystery in AI

More from Medium

Analysis of Anime Recommendations Dataset

Batch Import hydrograph from csv files into XPSWMM

Battery capacity estimation using Machine Learning : Part-2