Optimization of unsupervised affinity propagation clustering method

Jihan Alameddine; Kacem Chehdi; Claude Cariou

doi:10.1117/12.2533164

7 October 2019 Optimization of unsupervised affinity propagation clustering method

Jihan Alameddine, Kacem Chehdi, Claude Cariou

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 111550C (2019) https://doi.org/10.1117/12.2533164
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Affinity propagation (AP) is one of the most recent unsupervised classification methods used. Its property is very interesting, but its use reveals the existence of three drawbacks which seriously hamper its usage. These concern its sensitivity to: (i) the presence of identical objects in the population to be partitioned; (ii) the value of the preference parameter p and (iii) the value of the regularization parameter λ introduced for the updating of responsibilities and availabilities criteria. In this paper, an optimization of AP clustering method is proposed which provides an appropriate solution to each drawback underlined above. For the first two, we adapt the value of the preference parameter p for a given object, to the values of the similarities between this object and the others only. In the similarity matrix, any null distance for a given row is reassigned a preference value p, which corresponds to the minimum or average value of this row. Finally, to eliminate the regularization parameter while ensuring the convergence of the algorithm and the optimality of the solution, a smoothing operation on the elements of the responsibilities and the availabilities is introduced, involving the current estimated value and the three previous ones. This approach is tested on synthetic data and hyperspectral images and shows its efficiency. We show both the relevance of the results and their stability without the identified three drawbacks.

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Citation Download Citation

Jihan Alameddine, Kacem Chehdi, and Claude Cariou "Optimization of unsupervised affinity propagation clustering method", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550C (7 October 2019); https://doi.org/10.1117/12.2533164

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