NDLI: Unsupervised learning using locally linear embedding: experiments with face pose analysis

Content Provider	IEEE Xplore Digital Library
Author	Hadid, A. Kouropteva, O. Pietikainen, M.
Copyright Year	2002
Description	Author affiliation: Machine Vision Group, Oulu Univ., Finland (Hadid, A.; Kouropteva, O.; Pietikainen, M.)
Abstract	This paper considers a recently proposed method for unsupervised learning and dimensionality reduction, locally linear embedding (LLE). LLE computes a compact representation of high-dimensional data combining the major advantages of linear methods (computational efficiency, global optimality, and flexible asymptotic convergence guarantees) with the advantages of non-linear approaches (flexibility to learn a broad class on non-linear manifolds). We assess the performance of the LLE algorithm on real-world data (face images in different poses) and compare the results with those obtained with two different approaches (PCA and SOM). Extensions to the original LLE algorithm are proposed and applied to the problem of pose estimation.
Starting Page	111
Ending Page	114
File Size	253477
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1044625
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unsupervised learning Principal component analysis Face detection Machine vision Convergence Embedded computing Vectors Multidimensional systems Covariance matrix Organizing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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