About this document
Dimensionality Reduction Techniques by Cyril Lozada is a document available to read on EtoBox.
Dimensionality reduction techniques can reduce the number of dimensions in a dataset. The document discusses projecting a 3D dataset onto a 2D plane through dimensionality reduction, which reduces the number of points from 1000 to 100 and provides significant computational savings. Feature pruning is also presented as another dimensionality reduction method that removes unimportant features with low correlations to the output.
- Author
- Cyril Lozada
- Language
- EN