Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing

被引：13

作者：

Meng, Meng ^{[1
]}

Wei, Jia ^{[1
]}

Wang, Jiabing ^{[1
]}

Ma, Qianli ^{[1
]}

Wang, Xuan ^{[2
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China

[2] Harbin Inst Technol, Comp Applicat Res Ctr, Shenzhen Grad Sch, Shenzhen, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2017年 / 8卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Adaptive dimensionality reduction; Semi-supervised learning; Pairwise constraints weighting; Graph construction optimizing; EQUIVALENCE CONSTRAINTS; ILLUMINATION; RECOGNITION; FRAMEWORK; POSE;

D O I：

10.1007/s13042-015-0380-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the rapid growth of high dimensional data, dimensionality reduction is playing a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pairwise constraints. In this setting, domain knowledge is given in the form of pairwise constraints, which specifies whether a pair of instances belong to the same class (must-link constraint) or different classes (cannot-link constraint). In this paper, a novel semi-supervised dimensionality reduction method called adaptive semi-supervised dimensionality reduction (ASSDR) is proposed, which can get the optimized low dimensional representation of the original data by adaptively adjusting the weights of the pairwise constraints and simultaneously optimizing the graph construction. Experiments on UCI classification and image recognition show that ASSDR is superior to many existing dimensionality reduction methods.

引用

页码：793 / 805

页数：13