Self Organizing Maps (SOM) Research Paper Example | Topics and Well Written Essays - 2750 words

Self Organizing Maps (SOM) - Research Paper ExampleThe performance of ANNs is better than that of tralatitious methods of problem solving. This compounds a comprehensive understanding of the human cognitive abilities. From the available skill algorithms and neural network architectures, the SOM forms the most popular SOM. They example entropy visualization techniques by Teuvo Kohonen to reduce data dimension using self-organizing neural networks. The data visualization problems attempt to handle problems that are beyond human visualization for higher(prenominal) dimensional data. SOMs act as a non-parametric network checkering combination of data spatialization and abstraction, hence used in visual clustering. SOM is among the most popular methods of neural networks for use in cluster analysis. This occurs due to topology preserving and self organizing nature for SOM. The SOMs act as abstract model for topographic mapping. Modeling and analysis of mapping enhance understanding of perception, encoding, recognition and processes received and beneficial to the machine- base recognition of the patterns SOM possess prominent visualization properties. Developed from the associative memory model, SOM uses unsupervised learning algorithm characterized by simple computational form and structure enhanced by the retina-cortex mapping. The self-organization nature act as a fundamental process of pattern recognition, and allows learning the intra- and inter-pattern relationships for the stimuli without potential bias. SOM may provide the topologically preserved mapping to all the output spaces from scuttlebutt. Though the computational form proves to be simple, most aspects related to the algorithm must(prenominal) be investigated (Zhang et al 2010, p. 6359). 2.0 Basic principles of SOM The Kohonen self-organizing map encompasses a neural network, and various characteristics similar to the working of the human brain. Basically, SOM avails some classificatory resour ces that are organized based on patterns available for classification. The single layer of the neural network consists of neurons within n-dimensional grid. The grids allow the definition for the neighborhoods in the output space rather than the input space. The input and output spaces constitute the main SOM. This can also be performed through the use of tools that map vectors within the input space to output the space that preserve topological relations in the output space (Yang et al 2012, p.1371). SOM use unsupervised competitive learning and attempts to conform to the available input data. The SOM nodes act as inputs and contain some principle SOM features. Topological relationship between inputs is preserved after mapping into the SOM network. This pragmatically represents the complex data. SOMs use vector quantization in data crunch processes. The SOMs offer an appropriate means of representing the multi-dimensional data in the lower dimensional space using one or two dimen sions. This enhances visualization and understanding of data in low dimensions. Therefore, SOMs facilitates manipulation of complex data, especially in visualization of large quantities of data in an