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

Y. Guo, Junbin Gao, P. W. Kwan

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationAI 2009
Subtitle of host publicationAdvances in Artificial Intelligence
Place of PublicationGermany
PublisherSpringer
Pages240-249
Number of pages10
DOIs
Publication statusPublished - 2009
EventAustralian Joint Conference on Artificial Intelligence - Melbourne, VIC AUSTRALIA, Australia
Duration: 01 Dec 200904 Dec 2009

Conference

ConferenceAustralian Joint Conference on Artificial Intelligence
CountryAustralia
Period01/12/0904/12/09

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