pca
![Brian Piercy](data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="%230A66C2"><path d="M20.447 20.452H16.89v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433a1.982 1.982 0 1 1 0-3.964 1.982 1.982 0 0 1 0 3.964zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>)
Dive into Kernel PCA: explained with an example demonstrating its effectiveness compared to traditional PCA for nonlinear data.
Built-in function vs. numerical methods
An in-depth exploration of autoencoders and dimensionality reduction
Reducing the dimension of a dataset using methods such as PCA
Covariance, eigenvalues, variance and everything …
Sourced from O'Reilly ebook of the same name.
Modern AI systems approach tasks like recognising objects in images and predicting the 3D structure of proteins as a diligent student would prepare for an exam. By training on many example...
Simplest guide to PCA, ever.
At the beginning of the textbook I used for my graduate stat theory class, the authors (George Casella and Roger Berger) explained in the…
The code used to generate the plots for this post can be found here.
As data scientists or Machine learning experts, we are faced with tonnes of columns of data to extract insight from, among these features…
The purpose of this post is to give the reader detailed understanding of Principal Component Analysis with the necessary mathematical…
Short form: Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time. Part 1: the proper preparation of data (including…