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PAKDD
2005
ACM

Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning

10 years 9 months ago
Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning
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.
Junping Zhang, Stan Z. Li
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PAKDD
Authors Junping Zhang, Stan Z. Li
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