Probabilistic based recursive model for adaptive processing of data structures
Expert Systems with Applications: An International Journal
Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Cooperative node localization using nonlinear data projection
ACM Transactions on Sensor Networks (TOSN)
Learning a Self-organizing Map Model on a Riemannian Manifold
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
IEEE Transactions on Signal Processing
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Self-organizing potential field network: a new optimization algorithm
IEEE Transactions on Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
Visualization of multidimensional data in explorative forecast
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Self-organizing map (SOM) is an approach of nonlinear dimension reduction and can be used for visualization. It only preserves topological structures of input data on the projected output space. The interneuron distances of SOM are not preserved from input space into output space such that the visualization of SOM can be degraded. Visualization-induced SOM (ViSOM) has been proposed to overcome this problem. However, ViSOM is derived from heuristic and no cost function is assigned to it. In this paper, a probabilistic regularized SOM (PRSOM) is proposed to give a better visualization effect. It is associated with a cost function and gives a principled rule for weight-updating. The advantages of both multidimensional scaling (MDS) and SOM are incorporated in PRSOM. Like MDS, The interneuron distances of PRSOM in input space resemble those in output space, which are predefined before training. Instead of the hard assignment by ViSOM, the soft assignment by PRSOM can be further utilized to enhance the visualization effect. Experimental results demonstrate the effectiveness of the proposed PRSOM method compared with other dimension reduction methods.