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AI 2007: Advances in Artificial Intelligence, p. 659-663

DOI: 10.1007/978-3-540-76928-6_71

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

Journal article published in 2007 by Yi Guo, Kwan Ph, Junbin Gao ORCID, Paul Wing Hing Kwan
This paper is available in a repository.
This paper is available in a repository.

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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.