Schroedinger Eigenmaps for the Analysis of Biomedical Data

Author

Czaja, Wojciech ; Ehler, M.

Author_Institution

Dept. of Math., Univ. of Maryland, College Park, MD, USA

Volume

35

Issue

5

fYear

2013

fDate

41395

Firstpage

1274

Lastpage

1280

Abstract

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.

Keywords

eigenvalues and eigenfunctions; graph theory; learning (artificial intelligence); medical image processing; retinal recognition; SE; biomedical data analysis; graph Schroedinger operators; multispectral retinal images; schroedinger Eigenmaps; semisupervised manifold learning; Biomedical imaging; Eigenvalues and eigenfunctions; Kernel; Labeling; Laplace equations; Manifolds; Vectors; Laplacian Eigenmaps; Schroedinger Eigenmaps; Schroedinger operator on a graph; barrier potential; dimension reduction; manifold learning; Algorithms; Artificial Intelligence; Biomedical Research; Breast Neoplasms; Data Interpretation, Statistical; Data Mining; Databases, Factual; Female; Heart Diseases; Humans; Pattern Recognition, Automated; Retinal Diseases;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2012.270

Filename

6389681