Eficient indezing of high dimensional feature vectors is important to allow visual information systems and a number other applications to scale up to large databases. In this paper, we define this problem as “similarity indexing” and describe the fundamental types of “similarity queries” that we believe should be We also propose a new dynamic structure for similarity indexing called the similar...

We analyze the rate of convergence towards self-similarity for the subcritical KellerSegel system in the radially symmetric two-dimensional case and in the corresponding one-dimensional case for logarithmic interaction. We measure convergence in Wasserstein distance. The rate of convergence towards self-similarity does not degenerate as we approach the critical case. As a byproduct, we obtain a...

We analyze the rate of convergence towards self-similarity for the subcritical KellerSegel system in the radially symmetric two-dimensional case and in the corresponding one-dimensional case for logarithmic interaction. We measure convergence in Wasserstein distance. The rate of convergence towards self-similarity does not degenerate as we approach the critical case. As a byproduct, we obtain a...

Explicitly or implicitly, most of dimensionality reduction methods need to determine which samples are neighbors and the similarity between the neighbors in the original highdimensional space. The projection matrix is then learned on the assumption that the neighborhood information (e.g., the similarity) is known and fixed prior to learning. However, it is difficult to precisely measure the int...

We describe an artist recommendation system which integrates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic features, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measurements of subjective similarity between artists. By producing low-dimensional representations of artists, our syste...