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Neural Network Machine Learning and Dimension Reduction for Data VisualizationNeural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.
Document ID
20140002647
Acquisition Source
Langley Research Center
Document Type
Technical Memorandum (TM)
Authors
Liles, Charles A. (NASA Langley Research Center Hampton, VA, United States)
Date Acquired
April 2, 2014
Publication Date
March 1, 2014
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
L-20389
NASA/TM-2014-218181
NF1676L-18414
Funding Number(s)
WBS: WBS 346620.04.07.01.01.02
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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