首页|融合深度特征与多核学习的LSTWSVM及其工业应用

融合深度特征与多核学习的LSTWSVM及其工业应用

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为了提高多核学习(MKL)的表示能力同时降低其计算成本,提出一种融合深度特征与多核学习的最小二乘孪生支持向量机(LSTWSVM)算法.针对支持向量机等核分类器在多核学习中高计算复杂度的问题,提出一种基于边缘错误最小化原则的多核LSTWSVM框架,利用分类器优势提高多核学习的性能.针对高斯多核浅层结构的问题,采用MKL法设计一种基于深度神经网络多层信息的高鲁棒性深度映射核,将此深度核与多尺度高斯基核以核矩阵哈达玛积方式相融合,构造一组新的具有高度表达能力的改进核.最后,将基于LSTWSVM的多核训练算法与改进的多核结构进行高度集成,通过大量基准数据集与工业数据实验表明,其能有效结合深度学习与多核学习的优势,且以较低的计算成本提高分类精度与泛化能力.
LSTWSVM fusion of deep feature and multiple kernel learning and its industrial applications
In order to improve the representation ability of multi-kernel learning and reduce its computational cost,this paper proposes a least squares twin support vector machine(LSTWSVM)algorithm that combines depth feature and multi-kernel learning.Aiming at the problem of high computational complexity of kernel classifiers such as support vector machine in multi-kernel learning,a multi-kernel LSTWSVM framework based on the principle of edge error minimization is proposed.The cost-sensitive learning idea is adopted to improve the performance of multi-kernel learning by using the advantages of classifiers.Aiming at the problem of Gauss multi-kernel shallow structure,a highly robust depth mapping kernel based on depth neural network multi-layer information is designed using the MKL method.The depth kernel and multi-scale Gaussian basis kernel are fused in the form of kernel matrix Hadamard product to construct a new set of improved cores with high expressiveness,which contains the deep feature information of data.Finally,this paper highly integrates the multi-kernel training algorithm based on the LSTWSVM with the improved multi-kernel structure.Through benchmark datasets and industrial experiments,it shows that it can combine the advantages of deep learning and multi-kernel learning,and improve the classification accuracy and generalization ability at a lower computational cost.

multiple kernel learningdeep learningleast squares twin support vector machinecomplex industrial data modeling

刘颖、刘德彦、吕政、赵珺、王伟

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大连理工大学控制科学与工程学院,辽宁大连 116024

多核学习 深度学习 最小二乘孪生支持向量机 复杂工业数据建模

国家自然科学基金项目国家自然科学基金项目国家科技部重点研发计划项目中央高校基本科研业务费专项资金项目辽宁省应用基础研究计划项目

61873048620030722017YFA0700300DUT22JC162023JH2/101600043

2024

10.13195/j.kzyjc.2022.1746

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(8)