Twin kernel embedding with relaxed constraints on dimensionality reduction for structured data

Authors:
Yi Guo;Junbin Gao;Paul W. Kwan
Affiliations:
School of Science and Technology, University of New England, Armidale, NSW, Australia;School of Acc & Computer Science, Charles Sturt University, Bathurst, NSW, Australia;School of Science and Technology, University of New England, Armidale, NSW, Australia
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

This paper proposes a new nonlinear dimensionality reduction algorithm called RCTKE for highly structured data. It is built on the original TKE by incorporating a mapping function into the objective functional of TKE as regularization terms where the mapping function can be learned from training data and be used for novel samples. The experimental results on highly structured data is used to verify the effectiveness of the algorithm.