El Fattahi, L. and Sbai, E.H. (2018) Kernel entropy principal component analysis using Parzen estimator. In: UNSPECIFIED.

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Clustering is the task of dividing data objects into meaningful groups named as clusters such that objects in the same cluster are similar and objects form different clusters are dissimilar. It is an important unsupervised technique more and more frequently adopted by several research communities. In this paper we introduce an enhanced kernel-based method for data transformation. The method is founded on the maximum entropy principle through the kernel entropy principal component analysis. Incorporating the kernel method, the input space can be implicitly mapped into a high-dimensional feature space. Therefore the nonlinear patterns turn linear. The key measure is Shannon's entropy estimated via the inertia provided by the contribution of each object in data. As a result, the proposed method uses kernel mapping function to map data before performing entropy principal component analysis. Then data could be reduced into lower dimension of valuable extracted features. This has a major effect on the fast search of center clusters based on the local densities. The method performs very well our clustering algorithm. © 2018 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Clustering algorithms; Computer vision; Intelligent systems; Maximum entropy methods; Maximum principle; Metadata, clustering; Density peaks; kernel entropy principal component analysis (KEPCA); Maximum entropy principle; Parzen estimators, Principal component analysis
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2367

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