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The purpose of the article is to develop a new dimensionality reduction algorithm for data that are described by many features of different nature. A method of feature selection is based on a new concept of metrical importance of the features. The concept of feature importance is based on metrical properties of data and is inspired by the principle component analysis. Numerical experiments confirm the effectiveness of the method and certain accordance of it with other concepts of feature importance.

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Dimensionality Reduction in Product of Metric Spaces

  • 会议论文

  • 发表日期：

    2025年

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  • Aleksander Denisiuk

摘要

The purpose of the article is to develop a new dimensionality reduction algorithm for data that are described by many features of different nature. A method of feature selection is based on a new concept of metrical importance of the features. The concept of feature importance is based on metrical properties of data and is inspired by the principle component analysis. Numerical experiments confirm the effectiveness of the method and certain accordance of it with other concepts of feature importance.