Development of Generic CNN Deep Learning Method Using Feature Graph

we propose a method by applying Convolutional Neural Networks (CNN) to non-structured data. CNN has been successful in many fields such as image processing and speech recognition. On the other hand, it was difficult to adapt CNN to n non-structured data such as a csv file with multiple variables. The sequence of the data of the low dimensional grid structure such as the image has a meaning, and the CNN recognizes the order as the feature of the image and processes it. Due to this constraint, CNN could not perform feature recognition on non-structured data whose sequence can be reordered while leaving the meaning intact. In this work we developed a method to tackle this issue and make CNN applicable by endowing meaning to the sequence of non-structured data, and demonstrated its effectiveness by adding improvements.