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副研究员
招生学科专业:
动力工程及工程热物理 -- 【招收硕士研究生】 -- 能源与动力学院
航空宇航科学与技术 -- 【招收博士、硕士研究生】 -- 能源与动力学院
能源动力 -- 【招收博士、硕士研究生】 -- 能源与动力学院
学历:南京航空航天大学
学位:工学博士学位
所在单位:能源与动力学院
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Sparse kernel minimum squared error using Householder transformation and givens rotation
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所属单位:能源与动力学院
发表刊物:APPLIED INTELLIGENCE
关键字:Kernel method Kernel minimum squared error Householder transformation Givens rotation Sparseness
摘要:Two obvious limitations exist for baseline kernel minimum squared error (KMSE): lack of sparseness of the solution and the ill-posed problem. Previous sparse methods for KMSE have overcome the second limitation using a regularization strategy, which introduces an increase in the computational cost to determine the regularization parameter. Hence, in this paper, a constructive sparse algorithm for KMSE (CS-KMSE) and its improved version (ICS-KMSE) are proposed which will simultaneously address the two limitations described above. CS-KMSE chooses the training samples that incur the largest reductions on the objective function as the significant nodes on the basis of the Householder transformation. In contrast with CS-KMSE, there is an additional replacement mechanism using Givens rotation in ICS-KMSE, which results in ICS-KMSE giving better performance than CS-KMSE in terms of sparseness. CS-KMSE and ICS-KMSE do not require the regularization parameter at all before they begin to choose significant nodes, which is beneficial since it saves on the model selection time. More importantly, CS-KMSE and ICS-KMSE terminate their procedures with an early stopping strategy that acts as an implicit regularization term, which avoids overfitting and curbs the sparse level on the solution of the baseline KMSE. Finally, in comparison with other algorithms, both ICS-KMSE and CS-KMSE have superior sparseness, and extensive comparisons confirm their effectiveness and feasibility.
ISSN号:0924-669X
是否译文:否
发表时间:2018-02-01
合写作者:Xi, Peng-Peng,李兵,李智强
通讯作者:赵永平