Term tag: dimensionality reduction

Autoencoder

A neural network trained to reproduce its own input through a bottleneck too narrow to carry it — so the compressed code, not the copy, is the product.

Clustering

Grouping unlabeled data into clusters: which algorithm to pick, why each one imposes a shape, how to choose k, and how to tell if the clusters are real.

Dimensionality Reduction (DR)

Why high-dimensional data has to be compressed, how PCA picks its axes, and why cluster sizes and gaps in a t-SNE or UMAP plot mean nothing.

Feature Selection (FS)

Which columns to keep and how to decide: filter vs wrapper vs embedded, why 2^n subsets rules out brute force, and the CV mistake that fakes accuracy.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) reduces dimensions by projecting data onto orthogonal directions of maximum variance, ranked by the variance each keeps.

Unsupervised Learning

Finding structure in unlabeled data — no correct answers are given, so the model discovers groupings, patterns, or a compressed representation on its own.