Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time,...

We focus on implementing a nonlocal total variational method for unsupervised classification of hyperspectral imagery. We minimize the energy directly using a primal dual algorithm, which we modified for the non-local gradient and weighted centroid recalculation. By squaring the labeling function in the fidelity term before re-calculating the cluster centroids, we can implement an unsupervised ...

This study aimed to evaluate the clustering pattern consistency of soybean (Glycine max) lines, using seven different clustering methods. Our aim was to evaluate the best method for the identification of promising genotypes to obtain segregating populations. We used 51 generations F5 and F6 soybean lines originating from different hybridizations and backcrosses obtained from the soybean breedin...

Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2 norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the kNN query result may change over...

Nearest neighbor (NN) techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful in those cases exhibiting highly nonlinear relationship between variables. In most studies the distance measure is adopted a priori. In contrast, we propose a...

We study the performance of a transmission scheme employing random Gaussian codebooks and nearest neighbor decoding over a power limited additive non-Gaussian noise channel. We show that the achievable rates depend on the noise distribution only via its power and thus coincide with the capacity region of a white Gaussian noise channel with signal and noise power equal to those of the original c...

We consider the approximate nearest neighbor (ANN) problem where the input set consists of n k-flats in the Euclidean R, for any fixed parameters 0 ≤ k < d, and where, for each query point q, we want to return an input flat whose distance from q is at most (1 + ε) times the shortest such distance, where ε > 0 is another prespecified parameter. We present an algorithm that achieves this task wit...

We present a new regular grid search algorithm for quick fixed-radius nearest-neighbor lookup developed in Python. This module indexes set of k-dimensional points grid, with optional periodic conditions, providing fast approach nearest neighbors queries. In this first installment, we provide three types queries: bubble, shell and the nth-nearest. For these queries include different metrics inte...