ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 最优间隔分布脊回归

1. (计算机软件新技术国家重点实验室(南京大学) 南京 210023) (软件新技术与产业化协同创新中心 南京 210023) (chenjl@lamda.nju.edu.cn)
• 出版日期: 2017-08-01
• 基金资助:
国家自然科学基金项目(61673201)

### Optimal Margin Distribution Ridge Regression

Chen Jialüe， Jiang Yuan

1. (National Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023) (Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023)
• Online: 2017-08-01

Abstract: Ridge regression (RR) has been one of the most classical machine learning algorithms in many real applications such as face detection, cell prediction, etc. The ridge regression has many advantages such as convex optimization objection, closed-form solution, strong interpretability, easy to kernelization and so on. But the optimization objection of ridge regression doesn’t consider the structural relationship between instances. Supervised manifold regularized (MR) method has been one of the most representative and successful ridge regression regularized methods, which considers the instance structural relationship inter each class by minimizing each class’s variance. But considering the structural relationship interclasses alone is not a very comprehensive idea. Based on the recent principle of optimal margin distribution machine (ODM) learning with a novel view, we find the optimization object of ODM can include the local structural relationship and the global structural relationship by optimizing the margin variance interclasses and the margin variance intraclasses. In this thesis, we propose a ridge regression algorithm called optimal margin distribution machine ridge regression (ODMRR) which fully considers the structural character of the instance. Besides, this algorithm can still contain all the advantages of ridge regression and manifold regularized ridge regression. Finally, the experiments validate the effectiveness of our algorithm.