Adaptive nonlinear auto-associative modeling through manifold learning

Authors:
Junping Zhang;Stan Z. Li
Affiliations:
Intelligent Information Processing Laboratory, Department of Computer Science and Engineering, Fudan University, Shanghai, China;National Laboratory of Pattern Recognition & Center for Biometrics and Security Research Institute of Automation, CAS, Beijing, China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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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.