Self-Organizing Maps
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
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS
Journal of the American Society for Information Science and Technology
| Hi-index | 0.01 |
Most 2D visualization methods based on multidimensional scaling (MDS) and self-organizing maps (SOMs) use a symmetric distance matrix to represent and visualize object relationships in a data set. In many real-world applications, however, raw data such as a world-trade data are best captured as an asymmetric proximity matrix. Such asymmetric matrices cannot be perfectly represented by most previous methods. To handle such an intrinsic limitation, in this paper, we propose a dynamic learning for metric representations of asymmetric proximity data to better understand the data. The proposed learning generates two representations (maps) with the row vectors (sending or exporting) and column vectors (receiving or importing) of the matrix, respectively. To better present the patterns, we supplement the maps with two analysis tools: cluster analysis and distance analysis, which connect and compare the different patterns from the different maps. Experiment results using three real world data sets confirm that the proposed learning method is useful to understand asymmetric proximity data.