We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor ...

IPv6 Neighbor Discovery includes Neighbor Unreachability Detection. That function is very useful when a host has an alternative neighbor -for instance, when there are multiple default routers -since it allows the host to switch to the alternative neighbor in a short time. By default, this time is 3 seconds after the node starts probing. However, if there are no alternative neighbors, this timeo...

This paper analyzes k nearest neighbor classification with training data anonymized using anatomy. Anatomy preserves all data values, but introduces uncertainty in the mapping between identifying and sensitive values. We first study the theoretical effect of the anatomized training data on the k nearest neighbor error rate bounds, nearest neighbor convergence rate, and Bayesian error. We then v...

This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest ne...

Type Run Description MAP Official A UTen English ASR 0.0031 A UTt hs-t2-nm Top-2 concepts from t hs graph method with neighbor multiply 0.0137 A UTwiki-t2-nm Top-2 Wikipedia concepts with neighbor multiply 0.0131 A UTwiki-t2-en-nm Top-2 Wikipedia concepts and English ASR with neighbor multiply 0.0107 A UTwiki-t2-nl-nm Top-2 Wikipedia concepts and Dutch ASR with neighbor multiply 0.0096 A UTword...

Reverse nearest neighbor queries are useful in identifying objects that are of significant influence or importance. Existing methods either rely on pre-computation of nearest neighbor distances, do not scale well with high dimensionality, or do not produce exact solutions. In this work we motivate and investigate the problem of reverse nearest neighbor search on high dimensional, multimedia dat...

The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed. Editing of the preclassified samples using the three-nearest neighbor rule followed by classification using the single-nearest neighbor rule with the remaining preclassified samples appears to produce a decis...

IPv6 nodes use the Neighbor Discovery Protocol (NDP) to discover other nodes on the link, to determine their link-layer addresses to find routers, and to maintain reachability information about the paths to active neighbors. Secure Neighbor Discovery Protocol (SEND Protocol) is a security extension of Neighbor Discovery. The SEND protocol is designed to counter the threats to NDP. This paper pr...

Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The ma...