Given an initial rectangular range or k nearest neighbor (k-nn) query (using the L1 metric), we consider the problems of incrementally extending the query by increasing the size of the range, or by increasing k, and reporting the new points incorporated by each extension. Although both problems may be solved trivially by repeatedly applying a traditional range query or L1 k-nn algorithm, such s...

dimensional Euclidean space. Given N points {xj} in Rd, the algorithm attempts to find k nearest neighbors for each of xj , where k is a user-specified integer parameter. The algorithm is iterative, and its CPU time requirements are proportional to T ·N ·(d ·(log d)+ k · (log k) · (log N)) + N · k2 · (d + log k), with T the number of iterations performed. The memory requirements of the procedur...

1053-5888/08/$20.00©2008IEEE IEEE SIGNAL PROCESSING MAGAZINE [128] MARCH 2008 T he Internet has brought us a wealth of data, all now available at our fingertips. We can easily carry in our pockets thousands of songs, hundreds of thousands of images, and hundreds of hours of video. But even with the rapid growth of computer performance, we don’t have the processing power to search this amount of...

The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training an...

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that...

While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile device...

Recognizing aspects of articulation from audio recordings of speech is an important problem, either as an end in itself or as part of an articulatory approach to automatic speech recognition. In this paper we study the frame-level classification of a set of articulatory features (AFs) inspired by the vocal tract variables of articulatory phonology. We compare k nearest neighbor (k-NN) classifie...

The weighted k-nearest neighbors algorithm is one of the most fundamental nonparametric methods in pattern recognition and machine learning. The question of setting the optimal number of neighbors as well as the optimal weights has received much attention throughout the years, nevertheless this problem seems to have remained unsettled. In this paper we offer a simple approach to locally weighte...

We present the first sample compression algorithm for nearest neighbors with nontrivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify exis...

A data object is broad if it is one of the k-Nearest Neighbors (k-NN) of many data objects. We introduce a new database primitive called Generalized Nearest Neighbor (GNN) to express data broadness. We also develop three strategies to answer GNN queries efficiently for large datasets of multidimensional objects. The R*-Tree based search algorithm generates candidate pages and ranks them based o...