Glossary math Term Page

Low-Rank Approximation

An approximation using lower rank

low-rank #math#linear-algebra

Korean version

Aliases

low-ranklow-rank approximation

Prerequisites

Rank

Related Concepts

Rank Singular Value Singular Value Decomposition Dimensionality Reduction

Core Idea

A low-rank approximation replaces a matrix with another matrix of smaller rank while preserving as much important structure as possible. It is one of the main uses of singular value decomposition.

Role In This Blog

In Mathbong, low-rank approximation explains why SVD matters for compression, recommendation systems, and latent-structure modeling.

Posts Mentioning This Concept

[Linear Algebra Series Part 20] SVD and an Introduction to Dimensionality Reduction Use [[singular-value-decomposition|SVD]] to factor matrices, understand important directions, and connect low-rank approximation to [[dimensionality-reduction|dimensionality reduction]].[선형대수 시리즈 20편] 특이값 분해(SVD)와 차원축소 입문 특이값 분해(SVD)를 통해 행렬을 구조적으로 분해하고, 압축, 추천 시스템, 임베딩 차원축소와 연결합니다.