Point Symmetry-based Deep Clustering

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.