{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/moving-window-regression-a-novel-approach-to","title":"Moving Window Regression: A Novel Approach to Ordinal Regression","arxiv_id":"2203.13122","date":"2022-03-24","proceeding":"CVPR 2022 1","authors":["Nyeong-Ho Shin","Seon-Ho Lee","Chang-Su Kim"],"abstract":"A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank ($\\rho$-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors ($\\rho$-regressors) to predict $\\rho$-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 $\\rho$-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. 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