4 docs tagged with "dimensionality reduction"

Implement Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensional data while preserving its essential features. Visualize the transformed data to gain insights into underlying patterns.

In this post, we'll delve into Singular Value Decomposition (SVD), a matrix factorization technique used in linear algebra with applications in dimensionality reduction, image processing, and recommendation systems.

An overview of t-SNE, a popular technique for visualizing high-dimensional data in two or three dimensions.

This post explores t-SNE (t-distributed Stochastic Neighbor Embedding), a popular dimensionality reduction technique used to visualize high-dimensional data in a low-dimensional space.