An Incremental Locally Linear Embedding Algorithm with Non-Negative Constraints of the Weights

Locally Linear Embedding (LLE) is a batch method. When new sample is added, the whole algorithm must be run repeatedly and all the former computational results are discarded. In the paper, the LLE algorithm processing on new sample points is analyzed. For the insufficient precision of the processing...

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Bibliographic Details
Published in:Applied Mechanics and Materials Vol. 404; pp. 478 - 484
Main Authors: Xu, Qing Cheng, Hu, Jian Zhong
Format: Journal Article
Language:English
Published: Zurich Trans Tech Publications Ltd 01.09.2013
Subjects:
Incremental
Manifold Learning
Non-Negative Constraints
Locally Linear Embedding (LLE)
ISBN:3037858451, 9783037858455
ISSN:1660-9336, 1662-7482, 1662-7482
Online Access:Get full text
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Summary:Locally Linear Embedding (LLE) is a batch method. When new sample is added, the whole algorithm must be run repeatedly and all the former computational results are discarded. In the paper, the LLE algorithm processing on new sample points is analyzed. For the insufficient precision of the processing of traditional incremental LLE, an incremental LLE algorithm based on non-negative constraints of the weights is proposed. Non-negative constraints of linear weights are imposed on the new sample points in the projection process. The simple fitting of the original algorithm from the engineering application is avoided by the proposed algorithm and the problem of the constantly updating of the whole manifold is solved at the case of new samples being added. Compared with the traditional incremental LLE method, S-curve simulation data and engineering examples analysis show the feasibility and effectiveness of the proposed algorithm.
Bibliography:Selected, peer reviewed papers from the 2013 2nd International Symposium on Manufacturing Systems Engineering (ISMSE 2013), July 27-29, 2013, Singapore
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISBN:3037858451
9783037858455
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.404.478