Title
Solving Non-image Learning Problems by Mapping to Images
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
9-7-2020
Abstract
Transforming non-image Machine Learning (ML) problems into the image recognition problems, opens an opportunity to solve these problems, by powerful deep learning algorithms. This paper proposes a new CPC-R algorithm, which converts non-image data into images, by visualizing non-image data. Then several deep learning CNN algorithms are exploited to solve the learning problems. The CPC-R algorithm preserves high-dimensional information in 2-D. It splits the attributes of an n-D point into pairs of its values and visualizes pairs as 2-D points, in the same 2-D Cartesian coordinates. Next, it maps pairs to grey scale or color intensity values to encode the order of pairs producing a heatmap. This paper reports the results of computational experiments with CPC-R for different CNN architectures, and methods to optimize the CPC-R images. These results show that the combined CPC-R and deep learning CNN algorithms can solve non-image ML problems, at the state-of-the-art level of accuracy for the benchmark datasets.
Recommended Citation
Kovalerchuk, B., Agarwal, B., & Kall, D. C. (2020). Solving Non-image Learning Problems by Mapping to Images. 2020 24th International Conference Information Visualisation (IV). https://doi.org/10.1109/iv51561.2020.00050
Journal
2020 24th International Conference Information Visualisation (IV)
Rights
Copyright © 2020, IEEE
Comments
This article was originally published in 2020 24th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.
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