Papers › Nested Subspace Arrangement for Representation of Relational Data

Nested Subspace Arrangement for Representation of Relational Data

4 Jul 2020ICML 2020 1arXiv:2007.02007archive 2025-07-28

Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa

Studies on acquiring appropriate continuous representations of discrete objects, such as graphs and knowledge base data, have been conducted by many researchers in the field of machine learning. In this study, we introduce Nested SubSpace (NSS) arrangement, a comprehensive framework for representation learning. We show that existing embedding techniques can be regarded as special cases of the NSS arrangement. Based on the concept of the NSS arrangement, we implement a Disk-ANChor ARrangement (DANCAR), a representation learning method specialized to reproducing general graphs. Numerical experiments have shown that DANCAR has successfully embedded WordNet in ℝ²⁰ with an F1 score of 0.993 in the reconstruction task. DANCAR is also suitable for visualization in understanding the characteristics of graphs.

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