Back to Search
Start Over
V-SOINN: A topology preserving visualization method for multidimensional data.
- Source :
-
Neurocomputing . Aug2021, Vol. 449, p280-289. 10p. - Publication Year :
- 2021
-
Abstract
- Data visualization plays an important role in data analysis by displaying data to observers in an interpretable way. Visualizing multidimensional data requires projecting the data into a low-dimensional space that is visible to humans. In this paper, we propose a neural network model that can generate such projections while preserving the topology relationships within data points, which is named Visible Self Organizing Incremental Neural Network (V-SOINN). V-SOINN is able to construct a topology preserving visible network automatically and classify visible nodes to different classes in the low-dimensional space. The thought of topology preserving visualization stems from Self-Organizing Map (SOM). Compared to SOM, the main advantage of V-SOINN is that it does not need prior decision of network structure, including the number of nodes and grid in the output layer. V-SOINN can show the density distribution of datasets by using the activation counts of datasets. V-SOINN is able to depict the number of classes in the low-dimensional space as well. We perform experiments on artificial and real-world datasets, and V-SOINN outperforms PCA, MDS, t-SNE, Neural Gas and SOM on the datasets. Experiments show that V-SOINN can preserve the topology and V-SOINN can produce the correct classification result when the number of samples is small. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*SELF-organizing maps
*TOPOLOGY
*VISUALIZATION
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 449
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 150291812
- Full Text :
- https://doi.org/10.1016/j.neucom.2021.03.113