Semi-supervised Local Aggregation Methodology

In this paper we propose a novel approach for automatic mine detection in SONAR data. The proposed framework relies on possibilistic based fusion method to classify SONAR instances as mine or mine-like object. The proposed semi-supervised algorithm minimizes some objective function which combines context identification, multi-algorithm fusion criteria and a semi-supervised learning term. The optimization aims to learn contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic semi-supervised learning and feature discrimination. The semi-supervised clustering component assigns degree of typicality to each data sample in order to identify and reduce the influence of noise points and outliers. Then, the approach yields optimal fusion parameters for each context. The experiments on synthetic datasets and standard SONAR dataset show that our semi-supervised local fusion outperforms individual classifiers and unsupervised local fusion.