Using data images for outlier detection

The data image has been proposed as a method for visualizing high-dimensional data. The idea is to map the data into an image, by using gray-scale (or color) values to indicate the magnitude of each variate. Thus, the image for a data set of size n and dimension d is a d × η image, where the columns correspond to observations and the rows to variates. We consider the application of this idea to the detection of outliers, providing a simple visualization technique that highlights outliers and clusters within the data.