Converting tabular data into images for deep learning with convolutional neural networks.

ABSTRACT: Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expres

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC8166880 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature