Spotting Differences Among Observations.

Identifying differences among the sample distributions of different observations is an important issue in many fields ranging from medicine over biology and chemistry to physics. We address this issue, providing a general framework to detect difference spots of interest in feature space. Such spots occur not only at various locations, they may also come in various shapes and multiple sizes, even at the same location. We deal with these challenges in a scale-space detection framework based on the density function difference of the observations. Our framework is intended for semi-automatic processing, providing human-interpretable interest spots for further investigation of some kind, e.g., for generating hypotheses about the observations. Such interest spots carry valuable information, which we outline at a number of classification scenarios from UCI Machine Learning Repository; namely, classification of benign/malign breast cancer, genuine/forged money and normal/spondylolisthetic/disc-herniated vertebral columns. To this end, we establish a simple decision rule on top of our framework, which bases on the detected spots. Results indicate state-of-the-art classification performance, which underpins the importance of the information that is carried by these interest spots.