Classification and visualization based on derived image features: application to genetic syndromes.

ABSTRACT: Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates

SUBMITTER: Balliu B 

PROVIDER: S-EPMC4236018 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature