Multi-Dimensional Classification via Sparse Label Encoding

	Multi-Dimensional Classification via Sparse Label Encoding
	Jia, Bin-Bin1,2 ; Zhang, Min-Ling 1,3
	2021
会议名称	International Conference on Machine Learning (ICML)
会议录名称	INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
卷号	139
页码	4917-4926
会议日期	JUL 18-24, 2021
会议地点	ELECTR NETWORK
会议录编者/会议主办者	Apple ; ByteDance ; et al. ; Facebook AI ; Invenia Labs ; MAYO Clinic, Center for Individualized Medicine
出版地	SAN DIEGO
出版者	JMLR-JOURNAL MACHINE LEARNING RESEARCH
摘要	In multi-dimensional classification (MDC), there are multiple class variables in the output space with each of them corresponding to one heterogeneous class space. Due to the heterogeneity of class spaces, it is quite challenging to consider the dependencies among class variables when learning from MDC examples. In this paper, we propose a novel MDC approach named SLEM which learns the predictive model in an encoded label space instead of the original heterogeneous one. Specifically, SLEM works in an encoding-training-decoding framework. In the encoding phase, each class vector is mapped into a real-valued one via three cascaded operations including pairwise grouping, one-hot conversion and sparse linear encoding. In the training phase, a multi-output regression model is learned within the encoded label space. In the decoding phase, the predicted class vector is obtained by adapting orthogonal matching pursuit over outputs of the learned multi-output regression model. Experimental results clearly validate the superiority of SLEM against state-of-the-art MDC approaches.
关键词	Artificial intelligence Encoding (symbols) Regression analysis Signal encoding Class spaces Classification approach Encodings Label encoding Label space Learn+ Multi-dimensional classifications Multi-output Multiple class Regression modelling
收录类别	CPCI-S ; EI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000683104604085
EI入藏号	20232414206208
EI主题词	Decoding
EI分类号	716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 922.2 Mathematical Statistics
ISSN	2640-3498
原始文献类型	Conference article (CA)
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/150125
专题	电气工程与信息工程学院
通讯作者	Zhang, Min-Ling
作者单位	1.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Peoples R China; 2.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 3.Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China
第一作者单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Jia, Bin-Bin,Zhang, Min-Ling. Multi-Dimensional Classification via Sparse Label Encoding[C]//Apple, ByteDance, et al., Facebook AI, Invenia Labs, MAYO Clinic, Center for Individualized Medicine. SAN DIEGO:JMLR-JOURNAL MACHINE LEARNING RESEARCH,2021:4917-4926.