Cited by 14,965Face and Expression RecognitionNeural Networks and ApplicationsStochastic Gradient Optimization Techniques

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, Sam T., Saul, Lawrence K.·2000·Science

Abstract

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Crossref · 10.1126/science.290.5500.2323

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Authors
Roweis, S. T., & Saul, L. K.
Affiliations
  • Gatsby Computational Neuroscience Unit (GB)
  • AT&T (United States) (US)
Venue
Science
Published
2000-12-22
Volume
290
Issue
5500
Pages
2323-2326
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2053186076
Cited by
14,965
References
16
Citations by year
2026: 238 · 2025: 430 · 2024: 477 · 2023: 610 · 2022: 641 · 2021: 870 · 2020: 892 · 2019: 1005 · 2018: 922 · 2017: 836 · 2016: 819 · 2015: 845 · 2014: 832 · 2013: 806 · 2012: 747
FWCI
81.64
Type
journal article
Language
en
Open access
closed
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords
locally linear embedding, nonlinear dimensionality reduction, neighborhood preserving embedding, manifold learning, unsupervised learning, high-dimensional data visualization, face images, text documents
MeSH terms
Algorithms, Artificial Intelligence, Face, Humans, Mathematics, Pattern Recognition, Visual

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