We consider the Approximate Nearest Line Search (NLS) problem. Given a set L of N lines in the high dimensional Euclidean space R, the goal is to build a data structure that, given a query point q ∈ R, reports a line ` ∈ L such that its distance to the query is within (1+ ) factor of the distance of the closest line to the query point q. The problem is a natural generalization of the well-studi...

Canopy Gaps in an Upper Mississippi River floodplain plot were measured as part of a songbird nest-site selectivity study. Two methods of measuring floodplain forest canopy gaps were compared. One method used a ground crew to sweep the plot and record spatial and botanical information of canopy gaps > 10 meters in diameter. The second method used 1:15,000 scale color infrared stereoscopic aeria...

Motivated by applications in computer vision and databases, we introduce and study the Simultaneous Nearest Neighbor Search (SNN) problem. Given a set of data points, the goal of SNN is to design a data structure that, given a collection of queries, finds a collection of close points that are “compatible” with each other. Formally, we are given k query points Q = q1, · · · , qk, and a compatibi...

In recent years, researchers have found that palmprint is quite a promising biometric identifier as it has the merits of high distinctiveness, robustness, user friendliness, and cost effectiveness. Nearly all the existing palmprint recognition methods are based on one-to-one matching. However, recent studies have corroborated that matching based on image sets can usually lead to a better result...

Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern Recognition tasks. Their major advantage over other architectures is human readability of a model. However, they often gain poorer accuracy. Mostly used metric in SOM is the Euclidean distance, which is not the best approach to some problems. In this paper, we study an impact of the metric change on the SOM’s performance ...

Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dimensionality curse, referred to as hubness, that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set. Through theoretical and empiri...