Model-aware categorical data embedding: a data-driven approach

DOI10.1007/s00500-018-3170-5zbMath1398.68470OpenAlexW2801804234WikidataQ129916040 ScholiaQ129916040MaRDI QIDQ1797953

Chengzhang Zhu, Jianglong Song, Wentao Zhao, Jianping Yin, Qian Li, Xin-Wang Liu

Publication date: 22 October 2018

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-018-3170-5

zbMATH Keywords

support vector machinecategorical dataextreme learning machinedata-driven representationmodel-aware representation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Multi-view heterogeneous fusion and embedding for categorical attributes on mixed data

Uses Software

• t-SNE
• LMNN

Cites Work

• Kernels and distances for structured data
• Improved support vector machine algorithm for heterogeneous data
• Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number
• On Information and Sufficiency
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