Data Classification Based on the Features Reduction and Piecewise Linear Separation

2019 ◽  
pp. 282-289
Author(s):  
Iurii Krak ◽  
Olexander Barmak ◽  
Eduard Manziuk ◽  
Anatolii Kulias
Keyword(s):  
Piecewise Linear ◽  
Data Classification ◽  
Linear Separation ◽  
Features Reduction

Nonlinear Data Classification and Applications to Vehicle Calibration Identification

2016 ◽  
Author(s):  
Jing Wang ◽  
Yan Wang ◽  
Dimitar Filev ◽  
Lirjon Llusho
Keyword(s):  
Optimization Problems ◽  
Piecewise Linear ◽  
Data Classification ◽  
Boundary Curve ◽  
Detection Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Efficiency And Effectiveness ◽  
Vehicle Rollover ◽  
Fixed Structure

In this paper, we consider the general problem of data classification, and focus on the development of suitable approaches for automotive calibration applications. Specifically, we propose two methods for nonlinear data classification: the first one is to find the smallest piecewise linear (PWL) region for a given data set and the second one is to construct piecewise quadratic (PWQ) boundary to separate two data sets . In both methods, the construction of the boundary curve is formulated as convex optimization problems that can be solved efficiently. Our approaches can incorporate prior information about data distribution and allow fixed structure of the decision boundary from different data sets with similar generating sources. We demonstrate the efficiency and effectiveness of the approaches with an application to calibration identification of vehicle rollover detection algorithm.

Chaos in a manifold piecewise linear system

1981 ◽  
Vol 64 (10) ◽  
pp. 9-17 ◽  
Author(s):  
Toshimichi Saito ◽  
Hiroichi Fujita
Keyword(s):  
Linear System ◽  
Piecewise Linear ◽  
Piecewise Linear System
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