Neighborhood selection and eigenvalues for embedding data complex in low dimension

Authors:
Jiun-Wei Liou;Cheng-Yuan Liou
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taiwan
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2012

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Abstract

LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction. For LLE, the neighborhood selection approach is an important research issue. For different types of datasets, we need different neighborhood selection approaches to have better chance for finding reasonable representation within the required number of dimensions. In this paper, the ε-distance approach and a modified version of k-nn method are introduced. For LLE and Isomap, the eigenvectors obtained from these methods are much more discussed, but there are more information hidden in the corresponding eigenvalues which can be used for finding embeddings contains more data information.