By Topic

Manifold Learning and Missing Data Recovery through Unsupervised Regression

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

2 Author(s)
Carreira-Perpinan, M.A. ; EECS, Univ. of California, Merced, CA, USA ; Zhengdong Lu

We propose an algorithm that, given a high-dimensional dataset with missing values, achieves the distinct goals of learning a nonlinear low-dimensional representation of the data (the dimensionality reduction problem) and reconstructing the missing high-dimensional data (the matrix completion, or imputation, problem). The algorithm follows the Dimensionality Reduction by Unsupervised Regression approach, where one alternately optimizes over the latent coordinates given the reconstruction and projection mappings, and vice versa, but here we also optimize over the missing data, using an efficient, globally convergent Gauss-Newton scheme. We also show how to project or reconstruct test data with missing values. We achieve impressive reconstructions while learning good latent representations in image restoration with 50% missing pixels.

Published in:

Data Mining (ICDM), 2011 IEEE 11th International Conference on

Date of Conference:

11-14 Dec. 2011