RCMap: Efficiently Creating High-Quality Euclidean Embeddings

For many applications in computer vision and multimedia, similarity between objects is measured by a dissimilarity function that is complex, expensive to compute, and often non-metric. To allow fast distance computations, these objects may be embedded into a vector space, where the distance between the embedding of two objects approximates the actual dissimilarity between them. Traditional sparse embedding methods like FastMap and SparseMap allow embedding a new object into a vector space based on its dissimilarities with only a small set of objects. However, these methods do not optimize embedding quality, and may create embeddings that do not approximate the original dissimilarities well. BoostMap improves embedding quality, but incurs high computational cost. In this work, we propose RCMap, a technique that offers significant speedup over Boostmap, with minimal loss in embedding quality.