A Bayesian analysis of self-organizing maps
Neural Computation
Self-organization as an iterative kernel smoothing process
Neural Computation
On the distribution and convergence of feature space in self-organizing maps
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A polygonal line algorithm for constructing principal curves
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
Limitations of nonlinear PCA as performed with generic neural networks
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Pattern recognition in time series database: A case study on financial database
Expert Systems with Applications: An International Journal
Databases and the geometry of knowledge
Data & Knowledge Engineering
Kernel class-wise locality preserving projection
Information Sciences: an International Journal
Decoding Population Neuronal Responses by Topological Clustering
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
A WeVoS-CBR Approach to Oil Spill Problem
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A swarm-inspired projection algorithm
Pattern Recognition
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
ViSOM ensembles for visualization and classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Quality of adaptation of fusion ViSOM
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
A bio-inspired fusion method for data visualization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Journal of Medical Systems
Information Sciences: an International Journal
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The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requires a colouring scheme to imprint the distances between neurons so that the clustering and boundaries can be seen. Even though the distributions of the data and structures of the clusters are not faithfully portrayed on the map. Recently an extended SOM, called the visualisation-induced SOM (ViSOM) has been proposed to directly preserve the distance information on the map, along with the topology. The ViSOM constrains the lateral contraction forces between neurons and hence regularises the interneuron distances so that distances between neurons in the data space are in proportion to those in the map space. This paper shows that it produces a smooth and graded mesh in the data space and captures the nonlinear manifold of the data. The relationships between the ViSOM and the principal curve/surface are analysed. The ViSOM represents a discrete principal curve or surface and is a natural algorithm for obtaining principal curves/surfaces. Guidelines for applying the ViSOM constraint and setting the resolution parameter are also provided, together with experimental results and comparisons with the SOM, Sammon mapping and principal curve methods.