Embedding Nonlinear Dimensionality Reduction by Locally

نویسنده

  • Sam T. Roweis
چکیده

www.sciencemag.org (this information is current as of October 29, 2007 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/290/5500/2323 version of this article at: including high-resolution figures, can be found in the online Updated information and services, found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/290/5500/2323#related-content http://www.sciencemag.org/cgi/content/full/290/5500/2323#otherarticles , 3 of which can be accessed for free: cites 10 articles This article 363 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/290/5500/2323#otherarticles 26 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/comp_math Computers, Mathematics : subject collections This article appears in the following http://www.sciencemag.org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

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 کامل

Locally Linear Embedded Eigenspace Analysis

The existing nonlinear local methods for dimensionality reduction yield impressive results in data embedding and manifold visualization. However, they also open up the problem of how to define a unified projection from new data to the embedded subspace constructed by the training samples. Thinking globally and fitting locally, we present a new linear embedding approach, called Locally Embedded ...

متن کامل

An 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 کامل

Growing Locally Linear Embedding for Manifold Learning

Locally linear embedding is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper proposes a new manifold learning method, which is based on locally linear embedding and growing neural gas and is termed growing locally linear embedding (GLLE). GLLE overcomes the major limitations of the original locally linear emb...

متن کامل

Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine

The locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with suppor...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007