What are the algorithms for dimensionality reduction?

There are various dimensionality reduction algorithms that help decrease the number of features in data while preserving important information. The main ones include:

• PCA (Principal Component Analysis) — a linear method that projects data onto a new space with lower dimensionality, maximizing variance.

• t-SNE (t-Distributed Stochastic Neighbor Embedding) — a nonlinear method suitable for visualizing high-dimensional data in 2D or 3D.

• LDA (Linear Discriminant Analysis) — considers classes and finds a projection that maximizes separation between classes.

• Autoencoders — neural network models that learn to encode data into a compressed representation and reconstruct it back.

• Isomap, MDS (Multidimensional Scaling) — nonlinear dimensionality reduction methods based on preserving the geometry of data.

The choice of algorithm depends on the task: t-SNE is often used for visualization, PCA for preprocessing, LDA for classification tasks, etc.