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    Chapter and Conference Paper

    Reducing Hubness for Kernel Regression

    In this paper, we point out that hubness—some samples in a high-dimensional dataset emerge as hubs that are similar to many other samples—influences the performance of kernel regression. Because the dimension of ...

    Kazuo Hara, Ikumi Suzuki, Kei Kobayashi… in Similarity Search and Applications (2015)

  2. Chapter and Conference Paper

    Ridge Regression, Hubness, and Zero-Shot Learning

    This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a map** between the example space to the label space. Contrary to the existing approach, which attempts...

    Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara… in Machine Learning and Knowledge Discovery i… (2015)

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