文献类型：期刊文献

英文题名：A Multi-View Deep Metric Learning approach for Categorical Representation on mixed data

作者：Li, Qiude Ji, Shengfen Hu, Sigui Yu, Yang Chen, Sen Xiong, Qingyu Zeng, Zhu

第一作者：Li, Qiude

通信作者：Zeng, Z[1];Ji, SF[2]

机构：[1]Guizhou Med Univ, Sch Biol & Engn, Guiyang 550025, Guizhou, Peoples R China;[2]Guizhou Inst Technol, Sch Foreign Language, Guiyang 550003, Guizhou, Peoples R China;[3]Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China

第一机构：Guizhou Med Univ, Sch Biol & Engn, Guiyang 550025, Guizhou, Peoples R China

通信机构：corresponding author), Guizhou Med Univ, Sch Biol & Engn, Guiyang 550025, Guizhou, Peoples R China;corresponding author), Guizhou Inst Technol, Sch Foreign Language, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

年份：2023

卷号：260

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20225013239975);Scopus(收录号：2-s2.0-85143722421);WOS:【SCI-EXPANDED(收录号:WOS:000972132400001)】；

基金：We thank anonymous reviewers for their valuable comments and suggestions. The work was supported by the National NaturalScience Foundation of China (Grant No. 62166009, 12132006) , the Guizhou Provincial Natural Science Foundation, China (Grant No. ZK [2021] 333, ZK [2022] 350) , the Science and technology Foundation of Guizhou Provincial Health Commission (Grant No. gzwkj2023-258) , and the Ph.D. Research Startup Foundation of Guizhou Medical University (Grant No. 2020-051) .

语种：英文

外文关键词：Representation learning; Deep metric learning; Multi -view learning; Coupling learning; Mixed data

摘要：It is an important and challenging task to represent the categorical values in mixed data as numerical vectors with intrinsic features, by revealing the complex coupling relationships between the categorical values, attributes and samples. The majority of extant studies expose only one particular coupling relationship in depth, or fuse multiple coupling relationships by using shallow learning based on kernels. The former may not fully mine the essential features of the categorical data. The latter typically has some limitations, for example, difficulty in expanding the spatial structure and difficulty in deter-mining the optimal kernel function. Therefore, this paper proposes a Multi-view Deep Metric Learning for Categorical Representation on mixed data (MvDML-CR). Specifically, first, based on the principle of information complementarity, multiple coupled views are extracted from the complex interaction relationships of the categorical data. Then, in each coupled view, a new proxy loss function is designed to build a deep metric learning sub-model with strong separability, which represents the categorical values as numerical vectors with discrimination. Last, we employ the Hilbert-Schmidt independence criterion to maximize the dependency between the views, and then fuse the sub-models trained in the different views to enhance the complementarity and consistency of the categorical representations. Extensive experiments on 34 mixed datasets with diversified characteristics demonstrate that the classification performance of MvDML-CR is significantly improved, compared with the state-of-the-art competitors.(c) 2022 Elsevier B.V. All rights reserved.