Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning
Identifieur interne : 001255 ( Main/Merge ); précédent : 001254; suivant : 001256Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning
Auteurs : Junping Zhang [République populaire de Chine] ; Z. Li [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike traditional supervised manifold learning algorithm, the proposed ANAM algorithm has several advantages: 1) it implicitly embodies discriminant information because the suboptimal parameters of ANAM are determined based on error rate of the validation set. 2) it avoids the curse of dimensionality without loss accuracy because recognition is completed in the original space. Experiments on character and digit databases show that the advantages of the proposed ANAM algorithm.
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DOI: 10.1007/11430919_69
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