Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data

Author(s)
Guo, Yi
Gao, Junbin
Kwan, Paul H
Publication Date
2009
Abstract
In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.
Citation
AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings, p. 240-249
ISBN
9783642104381
364210438X
Link
https://hdl.handle.net/1959.11/5598
Language
en
Publisher
Springer
Series
Lecture Notes in Computer Science
Title
Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data
Type of document
Conference Publication
Entity Type
Publication

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