Saul Nonlinear Dimensionality Reduction by Locally Linear Embedding
نویسندگان
چکیده
clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles ): September 20, 2013 www.sciencemag.org (this information is current as of The following resources related to this article are available online at http://www.sciencemag.org/content/290/5500/2323.full.html version of this article at: including high-resolution figures, can be found in the online Updated information and services,
منابع مشابه
Grouping and dimensionality reduction by locally linear embedding
Locally Linear Embedding (LLE) is an elegant nonlinear dimensionality-reduction technique recently introduced by Roweis and Saul [2]. It fails when the data is divided into separate groups. We study a variant of LLE that can simultaneously group the data and calculate local embedding of each group. An estimate for the upper bound on the intrinsic dimension of the data set is obtained automatica...
متن کاملAn Introduction to Locally Linear Embedding
Many problems in information processing involve some form of dimensionality reduction. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover nonlinear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. No...
متن کاملNonlinear dimensionality reduction by locally linear embedding.
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-preservin...
متن کاملLawrence K . Saul Nonlinear Dimensionality Reduction by Locally Linear Embedding
, 2323 (2000); 290 Science Sam T. Roweis and Lawrence K. Saul Nonlinear Dimensionality Reduction by Locally Linear Embedding This copy is for your personal, non-commercial use only. clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to repu...
متن کاملThink Globally, Fit Locally: Unsupervised Learning of Nonlinear Manifolds
The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. The data, assum...
متن کامل