Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation

By Sebastijan Dumancic, Hendrik Blockeel. 2016

15 citations recorded by the source corpus.

The goal of unsupervised representation learning methods is to learn a new representation of the original data, such that it makes a certain classification task easier to solve. Since their introduction in late 2000s, these methods initiated a revolution within machine learning. In this paper we present an unsupervised representation learning method for relational data. The proposed approach uses a clustering procedure to learn a new representation. Moreover, we introduce an adaptive clustering method, capable of addressing multiple interpretations of similarity in relational context. Finally, we experimentally evaluate the proposed approach. The preliminary results show the promise of the approach, as the models learned on the new representation often achieve better performance and are less complex than the ones learned on the original data representation.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

DeepProbLog: Neural Probabilistic Logic Programming (2018, background): “Another research direction focuses on task-agnostic discovery of relational (symbolic) latent representations by exploiting approximate symmetries [54], a symbolic extension of the auto-encoding principle [55], or self-play [56].”
Demystifying Relational Latent Representations (2017, methodology): “As for the parameters of latent representation, we take the best parameters on individual datasets selected by the model selection procedure in [8].”