A circuit framework for robust manifold learning
Author
Summary, in English
Manifold learning and nonlinear dimensionality reduction addresses the problem of detecting possibly nonlinear structure in highdimensional data and constructing lower-dimensional configurations representative of this structure. A popular example is the Isomap algorithm which uses local information to approximate geodesic distances and adopts multidimensional scaling to produce lowerdimensional representations. Isomap is accurate on a global scale in contrast to many competing methods which approximate locally. However, a drawback of the Isomap algorithm is that it is topologically instable, that is, incorrectly chosen algorithm parameters or perturbations of data may drastically change the resulting configurations. We propose new methods for more robust approximation of the geodesic distances using a viewpoint of electric circuits. In this way, we achieve both the stability of local methods and the global approximation property of global methods, while compromising with local accuracy. This is demonstrated by a study of the performance of the proposed and competing methods on different data sets.
Department/s
Publishing year
2007
Language
English
Pages
323-332
Publication/Series
Neurocomputing
Volume
71
Issue
1-3
Document type
Journal article
Publisher
Elsevier
Topic
- Mathematics
Keywords
- Laplacian Eigenmaps
- Manifold learning
- Topological instability
- Multidimensional scaling
- Isomap
Status
Published
Research group
- Harmonic Analysis and Applications
ISBN/ISSN/Other
- ISSN: 0925-2312