Schroedinger Eigenmaps for the Analysis of Biomedical Data

被引：53

作者：

Czaja, Wojciech ^{[1
]}

Ehler, Martin ^{[2
,3
]}

机构：

[1] Univ Maryland, Dept Math, College Pk, MD 20742 USA

[2] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Biomath & Biometry, D-85764 Neuherberg, Germany

[3] Eunice Kennedy Shriver Natl Inst Child Hlth & Hum, NIH, Sect Med Biophys, Bethesda, MD 20892 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2013年 / 35卷 / 05期

基金：

美国国家科学基金会;

关键词：

Schroedinger Eigenmaps; Laplacian Eigenmaps; Schroedinger operator on a graph; barrier potential; dimension reduction; manifold learning; DIMENSIONALITY REDUCTION; BRUCHS MEMBRANE; DRUSEN; REGULARIZATION; DIAGNOSIS; TOOL;

D O I：

10.1109/TPAMI.2012.270

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce Schroedinger Eigenmaps (SE), a new semi-supervised manifold learning and recovery technique. This method is based on an implementation of graph Schroedinger operators with appropriately constructed barrier potentials as carriers of labeled information. We use our approach for the analysis of standard biomedical datasets and new multispectral retinal images.

引用

页码：1274 / 1280

页数：7

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[Anonymous], P IEEE INT C COMP VI

[2]

[Anonymous], 1997, P CBMS REGIONAL C SE

[3]

[Anonymous], 2007, P 24 INT C MACHINE L, DOI DOI 10.1145/1273496.1273532

[4] Exploiting manifold geometry in hyperspectral imagery [J].