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Unified Embedding and Clustering

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posted on 08.11.2021, 08:42 authored by Mebarka AllaouiMebarka Allaoui, Mohammed Lamine Kherfi, Abdelhakim Cheriet, Abdelhamid Bouchachia
In this paper, we introduce a novel algorithm that unifies manifold embedding and clustering (UEC) which efficiently predicts clustering assignments of the high dimensional data points in a new embedding space. The algorithm is based on a bi-objective optimisation problem combining embedding and clustering loss functions. Such original formulation will allow to simultaneously preserve the original structure of the data in the embedding space and produce better clustering assignments. The experimental results using a number of real-world datasets show that UEC is competitive with the state-of-art clustering methods.

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Email Address of Submitting Author

allaoui.mebarka@univ-ouargla.dz

ORCID of Submitting Author

0000-0002-1175-6087

Submitting Author's Institution

kasdi merbah ouargla university

Submitting Author's Country

Algeria

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