A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm

A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm

A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclas...

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Main Authors: Yuping Qin, Hamid Reza Karimi, Dan Li, Shuxian Lun, Aihua Zhang
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/894246
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spelling doaj-372f289e23404b03ab749ab824146fd52020-11-24T21:39:46ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/894246894246A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning AlgorithmYuping Qin0Hamid Reza Karimi1Dan Li2Shuxian Lun3Aihua Zhang4College of Engineering, Bohai University, Jinzhou 121013, ChinaDepartment of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, NorwayCollege of Mathematics and Physics, Bohai University, Jinzhou, ChinaNew Energy College, Bohai University, Jinzhou, ChinaCollege of Engineering, Bohai University, Jinzhou 121013, ChinaA Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.http://dx.doi.org/10.1155/2014/894246
collection DOAJ
language English
format Article
sources DOAJ
author Yuping Qin
Hamid Reza Karimi
Dan Li
Shuxian Lun
Aihua Zhang
spellingShingle Yuping Qin
Hamid Reza Karimi
Dan Li
Shuxian Lun
Aihua Zhang
A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
Abstract and Applied Analysis
author_facet Yuping Qin
Hamid Reza Karimi
Dan Li
Shuxian Lun
Aihua Zhang
author_sort Yuping Qin
title A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
title_short A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
title_full A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
title_fullStr A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
title_full_unstemmed A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
title_sort mahalanobis hyperellipsoidal learning machine class incremental learning algorithm
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2014-01-01
description A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.
url http://dx.doi.org/10.1155/2014/894246
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