Trapezoidal Segmented Regression: A Novel Continuous-scale Real-time Annotation Approximation Algorithm

Abstract

Accurate ground truth representations of human behavior and experiences are essential for furthering our understanding of the complex relationships between everyday events and interactions and their effects on people. Producing accurate ground truth signals for subjective or latent experiences is difficult because it requires human annotation and is subject to annotator bias, distraction artifacts, valuation errors, among others. We build on previous work aiming to produce highly accurate continuous-scale ground truth labels for human experiences which advocates using supplemental human observations to warp the continuous-scale annotations to correct these errors. We propose a new method, trapezoidal segmented regression, for optimally approximating fused human-produced continuous-scale annotations to simplify its segmentation into intervals of low and high confidence in valuation. We evaluate this algorithm as an alternative to the total variation denoising method used in prior work by comparing the ground truths that both methods produce in experiments where the true annotation target signal is known a priori. Results show that the proposed signal approximation technique performs on par with the prior method, producing ground truth signals in close alignment with the true target, but with the added advantages of being more easily tuned and intuitive. We conclude that the proposed algorithm enables accurate and more robust ground truth generation.