Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/5598
Title: Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data
Contributor(s): Guo, Yi (author); Gao, Junbin (author); Kwan, Paul H  (author)
Publication Date: 2009
DOI: 10.1007/978-3-642-10439-8_25
10.1007/978-3-642-10439-8
Handle Link: https://hdl.handle.net/1959.11/5598
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.
Publication Type: Conference Publication
Conference Name: 22nd Australasian Joint Conference Melbourne, Australia, 1st - 4th December, 2009
Conference Details: 22nd Australasian Joint Conference Melbourne, Australia, 1st - 4th December, 2009
Source of Publication: AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings, p. 240-249
Publisher: Springer-Verlag
Place of Publication: Berlin/Heidelburg, Germany
Field of Research (FOR): 080109 Pattern Recognition and Data Mining
080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Outcome Codes: 890202 Application Tools and System Utilities
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Series Name: Lecture Notes in Computer Science
Series Number : 5866
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