<pre><!--StartFragment-->The use of Self-Organizing Map (<span>SOM</span>) algorithm for feature extraction and dimensionality reduction applied to underwater object detection with Low Frequency Electromagnetic Waves is presented. Computer simulation is used to generate a direct model for the study region, and a Self Organizing Map Algorithm is used to fit the data and return a similar model, with smaller dimensionality and same characteristics. Results show that virtual sensors are created by the <span>SOM</span> algorithm with consistent predictions, filling the resolution gap of the input data. These results are useful for fastening decision making algorithms by reducing the number of inputs to a group of significant data.<!--EndFragment--></pre>
Improving Self-Organizing Maps with Unsupervised Feature Extraction
