6 docs tagged with "dimensionality-reduction"

Neural network-based dimensionality reduction: Encoder-Decoder architecture and bottleneck representations.

Reducing feature complexity while preserving information: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

A beginner-friendly explanation of Eigenvalues and Eigenvectors, their geometric meaning, and their critical role in dimensionality reduction (PCA) and data analysis.

Mastering feature extraction, variance preservation, and the math behind Eigenvalues and Eigenvectors.

A detailed explanation of Singular Value Decomposition (SVD), why it is the most general matrix decomposition, its geometric meaning, and its critical applications in dimensionality reduction and recommender systems.

Discovering patterns in unlabeled data through clustering, association, and dimensionality reduction.