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

Yi Guo, Junbin Gao, Paul Kwan

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)659-663
Number of pages5
JournalLecture Notes in Computer Science
Volume4830
Issue number2007
DOIs
Publication statusPublished - 2007

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