Twin Kernel Embedding with Relaxed Constraints on Dimensionality Reduction for Structured Data

Author(s)
Guo, Yi
Gao, Junbin
Kwan, Paul Hing
Publication Date
2007
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.
Citation
AI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence Gold Coast, Australia, December 2-6, 2007, p. 659-663
ISBN
9783540769262
Link
https://hdl.handle.net/1959.11/4513
Publisher
Springer
Title
Twin Kernel Embedding with Relaxed Constraints on Dimensionality Reduction for Structured Data
Type of document
Conference Publication
Entity Type
Publication

Files:

NameSizeformatDescriptionLink