Classification of High Dimension Low Sample-Size Data

Abstract

We propose a classification method for high dimension low sample-size data. High dimension low sample-size data (HDLSS data) is data in which the number of dimensions is much larger than the number of objects. Such data is commonly found amongst real world data such as microarray data or image data. However, it is well known that we tend to obtain poor classification results when applying conventional clustering methods which are based on distance for this type of data. In this paper, we propose an adaptable feature selection method and a related clustering method along with the results of several numerical examples to show the improved performance.