Machine Learning Independent of Population Distributions for Measurement

Many of recent advanced measurement techniques acquire highly complex information through elaborate measurement processes, and estimate the measurement objects from their corresponding patterns reflected in the outcome of these processes. The introduction of advanced statistical and machine learning methods to measurement techniques is now inevitable to solve these complicated inverse problems. However, the state of the art remains the straightforward application of problem settings and their solutions studied in statistics and machine learning which assume a steady population distribution providing the objective data, whereas every measurement is performed under a distinct distribution of disturbances and noises. As claimed and demonstrated in this paper, this fact largely degrades the accuracy and robustness of the measurements. To effectively overcome this issue, we present a framework of robust and accurate machine learning against deviations of population distributions between calibration data for the training and a new single observation in the measurement. This is achieved by properly reflecting generic measurement processes to their estimations. The significant advantages of the presented framework are demonstrated through a real-world application to olfactory sensing.