Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Content-based trademark retrieval system using visually salient features
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Visualization of high-dimensional data with relational perspective map
Information Visualization
The Geodesic Self-Organizing Map and its error analysis
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Learning a Self-organizing Map Model on a Riemannian Manifold
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
A Riemannian Self-Organizing Map
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Process state and progress visualization using self-organizing map
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.