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Unsupervised image embedding using nonparametric statistics
2008
Pattern Recognition (ICPR), Proceedings of the International Conference on
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduction algorithms assume that the examples densely populate the manifold. Image databases tend to break this assumption, having isolated islands of similar images instead. In this work, we propose a novel approach that embeds images into a low dimensional Euclidean space, while preserving local image similarities based on
doi:10.1109/icpr.2008.4761051
dblp:conf/icpr/MeiS08
fatcat:6cq2enmbjne5lbt3ki7whrupha