Multi-Modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse Multi-Kernel Learning and Distribution Adaption

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成果类型：

期刊论文

作者：

Zhang, Yuanpeng;Xia, Kaijian;Jiang, Yizhang;Qian, Pengjiang;Cai, Weiwei;...

通讯作者：

Xia, KJ

作者机构：

[Zhang, Yuanpeng; Qiu, Chengyu] Nantong Univ, Med Nursing Sch, Dept Med Informat, Nantong 226001, Jiangsu, Peoples R China.

[Xia, Kaijian; Lai, Khin Wee; Xia, KJ] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur 50603, Malaysia.

[Qian, Pengjiang; Jiang, Yizhang] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi 214122, Jiangsu, Peoples R China.

[Wu, Dongrui; Jiang, Yizhang] Huazhong Univ Sci & Technol, Lab Image Proc & Intelligent Control, Minist Educ, Wuhan 430074, Hubei, Peoples R China.

[Cai, Weiwei] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha 410004, Peoples R China.

通讯机构：

[Xia, KJ ] U

Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur 50603, Malaysia.

语种：

英文

关键词：

Brain modeling;Data models;EEG;Fuses;Kernel;manifold regularization;Multi-kernel learning;multi-modality fusion;Support vector machines;Training;Transfer learning;transfer learning

期刊：

IEEE/ACM Transactions on Computational Biology and Bioinformatics

ISSN：

1545-5963

年：

2022

卷：

期：

页码：

2387-2397

DOI：

10.1109/TCBB.2022.3142748

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 82072019, 62171203 and 62172192) 10.13039/501100004608-Natural Science Foundation of Jiangsu Province (Grant Number: BK20201441) Jiangsu Postdoctoral Research Funding Program (Grant Number: 2020Z020) Wuhan Science and Technology Bureau (Grant Number: 2020020601012240) 10.13039/501100003397-Huazhong University of Science and Technology Jiangsu Students’ Innovation and Entrepreneurship Training Program (Grant Number: 202110304096Y)

机构署名：

本校为其他机构

院系归属：

交通运输与物流学院

摘要：

With the development of sensors, more and more multimodal data are accumulated, especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis becomes very important and urgent. In this study, we combine multi-kernel learning and transfer learning, and propose a feature-level multi-modality fusion model with insufficient training samples. To be specific, we firstly extend kernel Ridge regression to its multi-kernel version under the lp-norm constraint to explore complementary patterns contained in multimodal data. Then we use marginal probability distribution adaption ...

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Multi-Modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse Multi-Kernel Learning and Distribution Adaption

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