NECA: Network-Embedded Deep Representation Learning for Categorical Data
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Abstract (arXiv)
We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data, NECA can support important downstream data mining tasks, such as clustering. Extensive experimental analysis demonstrated the effectiveness of NECA.
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References · 7
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- 2019A Gentle Introduction to Deep Learning for Graphscited by 107
- 2016Deep Learning over Multi-field Categorical Data: A Case Study on User Response Predictioncited by 67
- 2020Unsupervised Heterogeneous Coupling Learning for Categorical Representationcited by 13
- 2018Information Theory: A Tutorial Introductioncited by 7
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