NDLI: Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

Content Provider	IEEE Xplore Digital Library
Author	Feiping Nie Dong Xu Tsang, I.W.-H. Changshui Zhang
Copyright Year	1992
Abstract	We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F₀ = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F₀. Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.
Sponsorship	IEEE Signal Processing Society
Page Count	12
File Size	886135
Starting Page	1921
Ending Page	1932
File Format	PDF
ISSN	10577149
Volume Number	19
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Laboratories Linear discriminant analysis Linear regression Semisupervised learning Face recognition Intelligent systems Information science Automation Scattering semi-supervised learning Dimension reduction face recognition manifold embedding
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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