scholarly journals Fast Approximate Complete-data k-nearest-neighbor Estimation

2020 ◽  
Vol 49 (2) ◽  
pp. 18-30
Author(s):  
Alejandro Murua ◽  
Nicolas Wicker
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Complete Data ◽  
Fast Method ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Neighbor Graph ◽  
Very Large Datasets ◽  
Nearest Neighbor Graph

We introduce a fast method to estimate the complete-data set of k-nearest-neighbors.This is equivalent to finding an estimate of the k-nearest-neighbor graph of the data. The method relies on random normal projections. The k-nearest-neighbors are estimated by sorting points in a number of random lines. For very large datasets, the method is quasi-linear in the data size. As an application, we show that the intrinsic dimension of a manifold can be reliably estimated from the estimated set of k-nearest-neighbors in time about two orders of magnitude faster than when using the exact set of k-nearest-neighbors.

scholarly journals Fast k-Nearest-Neighbors Calculation for Interpolation of Radar Reflectivity Field*

2009 ◽  
Vol 26 (7) ◽  
pp. 1410-1414 ◽  
Author(s):  
Feng Gao
Keyword(s):  
Nearest Neighbor ◽  
Grid Point ◽  
Computational Cost ◽  
Nearest Neighbors ◽  
Radar Reflectivity ◽  
Fast Method ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Sampling Points ◽  
Real Time Applications

Abstract To efficiently implement the interpolation methods (e.g., Shepard’s method and its variants) for the radar reflectivity field, a fast method that calculates the k-nearest-neighbor nodes (sampling points in radar volume scan) of the interpolated point (grid point) is described and proved. Several geometric propositions of radar volume scan on which the method is based are suggested and proved. Finally, the computational cost of the method is analyzed. The method is fast enough for real-time applications.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

A Novel clustering method based on hybrid K-nearest-neighbor graph

2018 ◽  
Vol 74 ◽  
pp. 1-14 ◽  
Author(s):  
Yikun Qin ◽  
Zhu Liang Yu ◽  
Chang-Dong Wang ◽  
Zhenghui Gu ◽  
Yuanqing Li
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Clustering Method ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

Region-Based Graph Learning towards Large Scale Image Annotation

2012 ◽  
pp. 244-260
Author(s):  
Bao Bing-Kun ◽  
Yan Shuicheng
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Label Propagation ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph ◽  
Modeling Data

Graph-based learning provides a useful approach for modeling data in image annotation problems. In this chapter, the authors introduce how to construct a region-based graph to annotate large scale multi-label images. It has been well recognized that analysis in semantic region level may greatly improve image annotation performance compared to that in whole image level. However, the region level approach increases the data scale to several orders of magnitude and lays down new challenges to most existing algorithms. To this end, each image is firstly encoded as a Bag-of-Regions based on multiple image segmentations. And then, all image regions are constructed into a large k-nearest-neighbor graph with efficient Locality Sensitive Hashing (LSH) method. At last, a sparse and region-aware image-based graph is fed into the multi-label extension of the Entropic graph regularized semi-supervised learning algorithm (Subramanya & Bilmes, 2009). In combination they naturally yield the capability in handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets well validate the effectiveness and efficiency of the framework for region-aware and scalable multi-label propagation.

Improving the Performance of kNN in the MapReduce Framework Using Locality Sensitive Hashing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-16
Author(s):  
Sikha Bagui ◽  
Arup Kumar Mondal ◽  
Subhash Bagui
Keyword(s):  
Nearest Neighbor ◽  
Parallel Implementation ◽  
Block Size ◽  
Computation Time ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Mapreduce Framework ◽  
Data Set ◽  
Data Object ◽  
Very Large Datasets

In this work the authors present a parallel k nearest neighbor (kNN) algorithm using locality sensitive hashing to preprocess the data before it is classified using kNN in Hadoop's MapReduce framework. This is compared with the sequential (conventional) implementation. Using locality sensitive hashing's similarity measure with kNN, the iterative procedure to classify a data object is performed within a hash bucket rather than the whole data set, greatly reducing the computation time needed for classification. Several experiments were run that showed that the parallel implementation performed better than the sequential implementation on very large datasets. The study also experimented with a few map and reduce side optimization features for the parallel implementation and presented some optimum map and reduce side parameters. Among the map side parameters, the block size and input split size were varied, and among the reduce side parameters, the number of planes were varied, and their effects were studied.

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