Hierarchically Clustered Representation Learning

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The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings. we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.
Publisher
AAAI
Issue Date
2020-02-07
Language
English
Citation

34th AAAI Conference on Artificial Intelligence, AAAI 2020, pp.5776 - 5783

ISSN
2159-5399
URI
http://hdl.handle.net/10203/272456
Appears in Collection
IE-Conference Papers(학술회의논문)
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