Moving Window Regression: A Novel Approach to Ordinal Regression

2022/03/24 by Nyeong-Ho Shin, Shin, Nyeong-Ho, Seon‐Ho Lee +3 · 6 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition

paper · pdf · doi:10.48550/arxiv.2203.13122

openalex publication_date 2022/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (ρ-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (ρ-regressors) to predict ρ-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the ρ-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at https://github.com/nhshin-mcl/MWR.

Cited by

• Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models
• Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification
• Integrating ConvNeXt and Vision Transformers for Enhancing Facial Age Estimation
• Stage-wise Adaptive Label Distribution for Facial Age Estimation
• Application of machine learning to monster level prediction in tabletop RPG game design
• Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation

Related

• A Survey on Ordinal Regression: Applications, Advances and Prospects
• THOR: Threshold-Based Ranking Loss for Ordinal Regression
• Universal Domain Adaptation in Ordinal Regression
• Learning a binary search with a recurrent neural network. A novel approach to ordinal regression analysis
• Robust Deep Ordinal Regression Under Label Noise