Parallel Implementation of Nearest Feature Line and Rectified Nearest Feature Line Segment Classifiers Using OpenMP

2020 ◽  
pp. 31-40
Author(s):  
Ana-Lorena Uribe-Hurtado ◽  
Eduardo-José Villegas-Jaramillo ◽  
Mauricio Orozco-Alzate
Keyword(s):  
Line Segment ◽  
Parallel Implementation ◽  
Feature Line

A novel classifier based on shortest feature line segment

2011 ◽  
Vol 32 (3) ◽  
pp. 485-493 ◽  
Author(s):  
De-Qiang Han ◽  
Chong-Zhao Han ◽  
Yi Yang
Keyword(s):  
Line Segment ◽  
Feature Line

Neighborhood Feature Line Segment for Image Classification

2015 ◽  
Vol 25 (3) ◽  
pp. 387-398 ◽  
Author(s):  
Jeng-Shyang Pan ◽  
Qingxiang Feng ◽  
Lijun Yan ◽  
Jar-Ferr Yang
Keyword(s):  
Image Classification ◽  
Line Segment ◽  
Feature Line

Graph-preserving shortest feature line segment for dimensionality reduction

2013 ◽  
Vol 110 ◽  
pp. 80-91 ◽  
Author(s):  
Wei Li ◽  
Qiuqi Ruan ◽  
Jun Wan
Keyword(s):  
Dimensionality Reduction ◽  
Line Segment ◽  
Feature Line

scholarly journals Leave-one-out evaluation of the nearest feature line and the rectified nearest feature line segment classifiers using Multi-core architectures

2018 ◽  
Vol 14 (27) ◽  
pp. 75-99 ◽  
Author(s):  
Ana Lorena Uribe-Hurtado ◽  
Mauricio Orozco-Alzate ◽  
Eduardo-Jose Villegas-Jaramillo
Keyword(s):  
Parallel Algorithms ◽  
Line Segment ◽  
Single Instruction Multiple Data ◽  
Multiple Data ◽  
Feature Line ◽  
Computationally Expensive ◽  
Leave One Out ◽  
The Way

In this paper we present the parallelization of the leave-one-out test: areproducible test that is, in general, computationally expensive. Paral-lelization was implemented on multi-core multi-threaded architectures, us-ing the Flynn Single Instruction Multiple Data taxonomy. This techniquewas used for the preprocessing and processing stages of two classificationalgorithms that are oriented to enrich the representation in small samplecases: the nearest feature line (NFL) algorithm and the rectified nearestfeature line segment (RNFLS) algorithm. Results show an accelerationof up to 18.17 times with the smallest dataset and 29.91 times with thelargest one, using the most costly algorithm (RNFLS) whose complexityisO(n4). The paper also shows the pseudo-codes of the serial and parallel algorithms using, in the latter case, a notation that describes the way theparallelization was carried out as a function of the threads.

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