Feasibility Study on PUC for Measurement Noise Reduction

Abstract

The needs to employ machine learning is increasing for accurate estimation and noise reduction in recent advanced measurement where its output data is enormous, complex and noisy. Particularly, the recently emerging Positive and Unlabeled Classification (PUC) can be used to classify target objects and contaminants in the measurement. However, the existing standard machine learning is based on Bayesian estimation which assumes invariance of the target population distributions, whereas they are very different depending on the objects in the measurement. In this study, we investigated the PUC to overcome this issue. We applied the method to an actual measurement problem and confirmed its significant noise reduction.