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Communication Dans Un Congrès Année : 2018

Point Symmetry-based Deep Clustering

Résumé

Clustering is a central task in unsupervised learning. Recent advances that perform clustering into learned deep features (such as DEC[14], IDEC [6] or VaDe [10]) have shown improvements over classical algorithms, but most of them are based on the Euclidean distance. Moreover, symmetry-based distances have shown to be a powerful tool to distinguish symmetric shapes -- such as circles, ellipses, squares, etc. This paper presents an adaptation of symmetry-based distances into deep clustering algorithms, named SymDEC. Our results show that the proposed strategy outperforms significantly the existing Euclidean-based deep clustering as well as recent symmetry-based algorithms in several of the synthetic symmetric and UCI studied datasets.
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Dates et versions

hal-02982608 , version 1 (28-10-2020)

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Citer

Jose G. Moreno. Point Symmetry-based Deep Clustering. 27th ACM International Conference on Information and Knowledge Management - CIKM 2018, Oct 2018, Turin, Italy. pp.1747--1750, ⟨10.1145/3269206.3269328⟩. ⟨hal-02982608⟩
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