Correlation classifiers based on data perturbation: New formulations and algorithms. (April 2020)

• Record Type: Journal Article
• Authors: Liang, Zhizheng; Chen, Xuewen; Zhang, Lei; Liu, Jin; Zhou, Yong
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Correlation classifiers -- data perturbation -- ϕ divergence -- PMMO -- Data classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107106
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper develops a novel framework for a family of correlation classifiers. A family of correlation classifiers are designed from the pessimistic viewpoint under possible perturbation of data. The proximal majorization-minimization optimization (PMMO) is used to solve the proposed model. The convergence rate of the algorithm in terms of different criteria is given. The experiments have been conducted to demonstrate the effectiveness of the proposed approach. Abstract: This paper develops a novel framework for a family of correlation classifiers that are reconstructed from uncertain convex programs under data perturbation. Under this framework, correlation classifiers are exploited from the pessimistic viewpoint under possible perturbation of data, and the max-min optimization problem is formulated by simplifying the original model in terms of adaptive uncertainty regions. The proposed model can be formulated as a minimization problem under proper conditions. The proximal majorization-minimization optimization (PMMO) based on Bregman divergences is devised to solve the proposed model that may be nonconvex or nonsmooth. It is found that using PMMO to solve the proposed model can exploit the convergence rate of the solution sequence in the nonconvex case. In the case of specific functions we can use the accelerated versions of first-order methods to solve the proposed model with convexity in order to make them have fast convergence rates in terms of the objective function. Extensive experiments on some data sets are conducted to demonstrate the feasibility and validity of the proposed model.