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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