Manifold learning, a promised land or work in progress

Manifold learning, a promised land or work in progress (2005)

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by Mei-chen Yeh , I-hsiang Lee , Gang Wu , Yi Wu , Edward Y. Chang

Venue:	In Proc. of IEEE Conf. on Multimedia and Expo
Citations:	2 - 0 self

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Datum	Value	Source
TITLE	Manifold learning, a promised land or work in progress	INFERENCE
AUTHOR NAME	Mei-chen Yeh	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Electrical & Computer Engineering 1 and Computer Science 2, UC Santa Barbara	SVM HeaderParse 0.2
AUTHOR NAME	I-hsiang Lee	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Electrical & Computer Engineering 1 and Computer Science 2, UC Santa Barbara	SVM HeaderParse 0.2
AUTHOR NAME	Gang Wu	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Electrical & Computer Engineering 1 and Computer Science 2, UC Santa Barbara	SVM HeaderParse 0.2
AUTHOR NAME	Yi Wu	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Electrical & Computer Engineering 1 and Computer Science 2, UC Santa Barbara	SVM HeaderParse 0.2
AUTHOR NAME	Edward Y. Chang	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Electrical & Computer Engineering 1 and Computer Science 2, UC Santa Barbara	SVM HeaderParse 0.2
ABSTRACT	Tasks of image clustering and classification often deal with data of very high dimensions. To alleviate the dimensionality curse, several methods, such as Isomap, LLE and KPCA, have recently been proposed and applied to learn low-dimensional non-linear embedded manifolds in high-dimensional spaces. Unfortunately, the scenarios in which these methods appear to be effective are very contrived. In this work, we empirically examine these methods on a realistic but not-so-difficult dataset. We discuss the promises and limitations of these dimensionreduction schemes. 1.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	In Proc. of IEEE Conf. on Multimedia and Expo	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	10 found	ParsCit 1.0