By Topic

Visualization and synthesis of data using manifold learning based on Locally Linear Embedding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Alvarez-Meza, A.M. ; Signal Process. & Recognition Group, Univ. Nac. de Colombia sede Manizales, Manizales, Colombia ; Valencia-Aguirre, J. ; Daza-Santacoloma, G. ; Acosta-Medina, C.D.
more authors

In this paper we analyze high-dimensional data by means of the manifold learning algorithm Locally Linear Embedding. We employ this method to visually analyze both artificial and real-world datasets lying on nonlinear structures, comparing its transformations against the traditional feature extraction technique Principal Components Analysis. Moreover, we propose a data synthesis scheme based on manifold learning that allows to represent the observations in a low-dimensional space, and then we learn the underlying data structure to properly infer unknown samples. The synthesis results are compared against an interpolation technique that directly estimates unknown samples in the input space. According to the obtained results, the employed manifold learning method improves the data representability, suitably computing low-dimensional transformations of the observations, and properly synthesizing new samples with low relative errors.

Published in:

Computing Congress (CCC), 2011 6th Colombian

Date of Conference:

4-6 May 2011