Solving Non-image Learning Problems by Mapping to Images

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 sev...

Full description

Saved in:
Bibliographic Details
Main Authors: Kovalerchuk, Boris, Agarwal, Bedant, Kall, Divya Chandrika
Format: Conference Proceeding
Language:English
Subjects:
Compute Vision
Computer architecture
Convolutional Neutral Networks
Data conversion
Data visualization
Deep learning
Heating systems
Image color analysis
Image recognition
Machine Learning
Machine learning algorithms
Non-image data
Raster images
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2375-0138
DOI:10.1109/IV51561.2020.00050