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.
Original language | English |
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Pages (from-to) | 659-663 |
Number of pages | 5 |
Journal | Lecture Notes in Computer Science |
Volume | 4830 |
Issue number | 2007 |
DOIs | |
Publication status | Published - 2007 |