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We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in Rd, our… Expand Sign-random-projection locality-sensitive hashing (SRP-LSH) is a probabilistic dimension reduction method which provides an… Expand Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest… Expand Non-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However… Expand It is well known that high-dimensional nearest neighbor retrieval is very expensive. Dramatic performance gains are obtained… Expand Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed… Expand We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the… Expand This lecture note describes a technique known as locality-sensitive hashing (LSH) that allows one to quickly find similar entries… Expand We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p… Expand Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality… Expand