Embedded Map Projection for Dimensionality Reduction-Based Similarity Search

We describe a dimensionality reduction method based on data point projection in an output space obtained by embedding the Growing Hierarchical Self Organizing Maps (GHSOM) computed from a training data-set. The dimensionality reduction is used in a similarity search framework whose aim is to efficiently retrieve similar objects on the basis of the Euclidean distance among high dimensional feature vectors projected in the reduced space. This research is motivated by applications aimed at performing Document Image Retrieval in Digital Libraries. In this paper we compare the proposed method with other dimensionality reduction techniques evaluating the retrieval performance on three data-sets.