Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks.

ABSTRACT: Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the pre

SUBMITTER: Bazgir O 

PROVIDER: S-EPMC7463019 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature