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Showing 1 to 20 of 364 for “"nearest neighbor"”.
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Approximate nearest neighbor problem in high dimensions
… the problem of finding the approximate nearest neighbor when the data set points are the substrings of a given text T. The exact version of this problem is defined as follows. Given a text T of length n, we want to build a data structure that supports the following operation: given a …
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Approximate nearest neighbor and its many variants
… investigates two variants of the approximate nearest neighbor problem. First, motivated by the recent research on diversity-aware search, we investigate the k-diverse near neighbor reporting problem. The problem is defined as follows: given a query point q, report the maximum diversity set S …
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Nearest neighbor search for cyro-electron microscopy images
… part of the thesis, we try four different k-nearest neighbors search (KNNS) algorithms on cryo-EM projection images classifying them based on the viewing directions. Brute force search (BFS) could be very time consuming when the dataset is large. Therefore, strategies like locality sensitive …
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Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications
… and so on. On these large scale data sets, nearest neighbor search is fundamental for lots of applications including content based search/retrieval, recommendation, clustering, graph and social network research, as well as many other machine learning and data mining problems. Exhaustive …
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Efficient nearest-neighbor search algorithms for sub-Riemannian geometries
… building block shared by these algorithms: nearest-neighbor search. While nearest-neighbor search is known as the asymptotically dominant bottleneck of sampling-based planners, popular algorithms to efficiently identify neighbors are limited to robots capable of unconstrained motions, …
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Prosthesis control using a nearest neighbor electromyographic pattern classifier
A prosthesis control strategy using a nearest neighbor electromyographic pattern classifier was investigated with both a real time microprocessor-based controller and offline computational facilities. Four active electrodes for myoelectric signal amplitude detection were interfaced with a …
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Nearest neighbor search : the old, the new, and the impossible
… for dealing with massive dataset is the Nearest Neighbor (NN) problem. In the NN problem, the goal is to preprocess a set of objects, so that later, given a query object, one can find efficiently the data object most similar to the query. This problem has a broad set of applications in …
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Nonlinear optical processes and the nearest neighbor distribution in rubidium vapor
… optical process of PFWM to interrogate the nearest neighbor distribution (NND), a new analytical derivation for the NND in the non-interacting particle approximation is presented, along with the results of molecular dynamics simulations of the NND in rubidium vapor for realistic pair …
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Exploring Techniques for Providing Privacy in Location-Based Services Nearest Neighbor Query
… for the two main models namely: the snapshot nearest neighbor query model and the continuous nearest neighbor query model. First, we address snapshot nearest neighbor query model where location-based services response represents a snapshot of point in time. In this model, we introduce a novel …
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ParChain: A Framework for Parallel Hierarchical Agglomerative Clustering using Nearest-Neighbor Chain
… ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required …
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Understanding Indexing Efficiency for Approximate Nearest Neighbor Search in High-dimensional Vector Databases
… vectors to a given query vector, known as 𝑘-Nearest-Neighbor (𝑘-NN) search. Due to massive data scale in practice, Approximate Nearest-Neighbor (ANN), which builds a search index offline to accelerate search online, is often used instead. One of the most promising ANN indexing approaches is …
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Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Graph-based approaches to nearest neighbor search are popular and powerful tools for handling large datasets in practice, but they have limited theoretical guarantees. We study the worst-case performance of recent graph-based approximate nearest neighbor search algorithms, such as HNSW, NSG and …
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A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest
… requirements.</p> <p>This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature …
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A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is …
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Increasing the Precision of Forest Area Estimates through Improved Sampling for Nearest Neighbor Satellite Image Classification
… of three mosaicked Landsat ETM+ images with the nearest neighbor decision rule were explored. Large training data pools of single pixels were used in simulations to create samples with three sampling methods (random, stratified random, and systematic) and eight sample sizes (25, 50, 75, 100, 200, …
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Using a Nearest Neighbor Algorithm and Logistic Regression to Assess Hazard Identification in the U.S. Army Risk Management Process
… using data mining techniques, in particular Nearest Neighbor (NN) algorithm and Logistic Regression Model (LRM). NN determines how similar a participant's case is to an expert case and LRM analyzes the outputs in a way that allows us to see if any of the seven experiential and demographic …
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Wavelet Factorization and Related Polynomials
… (FIR) filters. In particular, we focus on nearest neighbor factorizations discussed by Wickerhauser and Zhu that allow for efficient implementation of the discrete wavelet transform (DWT) for the algorithms of Daubechies and Sweldens and Mallat. Nearest neighbor lifting is a specific form …
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