Dimensionality Reduction
Dimensionality reduction is the process of transforming data with many input variables into a lower-dimensional representation that preserves as much meaningful structure as possible.
6 resources across 2 libraries
Glossary Terms(5)
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables from a dataset to use in building a machine learning model, discardi…
Dimensionality Reduction
Dimensionality reduction is the process of transforming data with many input variables into a lower-dimensional representation that preserves as much meaningfu…
Principal Component Analysis
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated compo…
Autoencoder
An autoencoder is a type of neural network trained to reconstruct its own input by first compressing it into a smaller latent representation (encoding) and the…
Variational Autoencoder
A Variational Autoencoder (VAE) is a generative neural network that learns a probabilistic latent representation of data, enabling it to both reconstruct input…