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Dimensionality reduction: An interpretation from manifold regularization perspective

文献类型：期刊论文

作者Fan, Mingyu1; Gu, Nannan2; Qiao, Hong3; Zhang, Bo4,5
刊名INFORMATION SCIENCES
出版日期2014-09-01
卷号277页码:694-714
关键词Dimensionality reduction Manifold regularization Feature mapping Manifold learning Out-of-sample extrapolation
英文摘要In this paper, we propose to unify various dimensionality reduction algorithms by interpreting the Manifold Regularization (MR) framework in a new way. Although the MR framework was originally proposed for learning, we utilize it to give a unified treatment for many dimensionality reduction algorithms from linear to nonlinear, supervised to unsupervised, and single class to multi-class approaches. In addition, the framework can provide a general platform to design new dimensionality reduction algorithms. The framework is expressed in the form of a regularized fitting problem in a Reproducing Kernel Hilbert Space. It consists of one error part and two regularization terms: the complexity term and the smoothness term. The error part measures the difference between the estimated (low-dimensional) data distribution and the true (high-dimensional) data distribution or the difference between the estimated and targeted low-dimensional representations of data, the complexity term is a measurement of the complexity of the feature mapping for dimensionality reduction, and the smoothness term reflects the intrinsic structure of data. Based on the framework, we propose a Manifold Regularized Kernel Least Squares (MR-KLS) method which can efficiently learn an explicit feature mapping (in the semi-supervised sense). Experiments show that our approach is effective for out-of-sample extrapolation. (C) 2014 Elsevier Inc. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Information Systems
研究领域[WOS]Computer Science
关键词[WOS]PRESERVING PROJECTIONS ; DISCRIMINANT-ANALYSIS ; FACE RECOGNITION ; GEOMETRIC FRAMEWORK ; DATA VISUALIZATION ; GENERAL FRAMEWORK ; IMAGE MANIFOLDS ; DIFFUSION MAPS ; EIGENMAPS ; EXTENSIONS
收录类别SCI
语种英语
WOS记录号WOS:000338390200043
源URL[http://ir.ia.ac.cn/handle/173211/3034]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位1.Wenzhou Univ, Coll Math & Informat Sci, Wenzhou 325035, Zhejiang, Peoples R China
2.Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Chinese Acad Sci, AMSS, LSEC, Beijing 100190, Peoples R China
5.Chinese Acad Sci, AMSS, Inst Appl Math, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Mingyu,Gu, Nannan,Qiao, Hong,et al. Dimensionality reduction: An interpretation from manifold regularization perspective[J]. INFORMATION SCIENCES,2014,277:694-714.
APA Fan, Mingyu,Gu, Nannan,Qiao, Hong,&Zhang, Bo.(2014).Dimensionality reduction: An interpretation from manifold regularization perspective.INFORMATION SCIENCES,277,694-714.
MLA Fan, Mingyu,et al."Dimensionality reduction: An interpretation from manifold regularization perspective".INFORMATION SCIENCES 277(2014):694-714.

入库方式： OAI收割

来源：自动化研究所

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