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NECA: Network-Embedded Deep Representation Learning for Categorical Data. (arXiv:2205.12752v1 [cs.LG])
May 26, 2022, 1:10 a.m. | Xiaonan Gao, Sen Wu, Wenjun Zhou
cs.LG updates on arXiv.org arxiv.org
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.
arxiv data embedded learning network representation representation learning
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