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Integrated Dimensionality Reduction Technique for Mixed Data Involving Categorical Values

Author

Listed:
  • Chung-Chian Hsu

    (National Yunlin University of Science and Technology Yunlin, Taiwan)

  • Wei-Hao Huang

    (National Yunlin University of Science and Technology Yunlin, Taiwan)

Abstract

An extension to the recent dimensionality-reduction technique t-SNE is proposed. The extension facilitates t-SNE to handle mixed-type datasets. Each attribute of the data is associated with a distance hierarchy which allows the distance between numeric values and between categorical values be measured in a unified manner. More importantly, domain knowledge regarding semantic distance between categorical values can be specified in the hierarchy. Consequently, the extended t-SNE can reflect topological order of the high-dimensional, mixed data in the low-dimensional space.

Suggested Citation

  • Chung-Chian Hsu & Wei-Hao Huang, 2014. "Integrated Dimensionality Reduction Technique for Mixed Data Involving Categorical Values," Human Capital without Borders: Knowledge and Learning for Quality of Life; Proceedings of the Management, Knowledge and Learning International Conference 2014,, ToKnowPress.
    • Handle: RePEc:tkp:mklp14:245-255
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