基于缓变特征学习的判别有序回归

doi:10.3969/j.issn.1006-2475.2016.07.005

计算机与现代化 ›› 2016, Vol. 251 ›› Issue (07): 24-27,32.doi: 10.3969/j.issn.1006-2475.2016.07.005

基于缓变特征学习的判别有序回归

南京航空航天大学计算机科学与技术学院，江苏南京210016

收稿日期:2016-01-07 出版日期:2016-07-21 发布日期:2016-07-22
作者简介: 李亚克（1990-），男，河南新乡人，南京航空航天大学计算机科学与技术学院硕士研究生，研究方向：机器学习，模式识别；高航（1964-），男，福建平潭人，副教授，硕士生导师，研究方向：模式识别，物联网应用。

Slow Feature Learning Discriminant for Ordinal Regression

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2016-01-07 Online:2016-07-21 Published:2016-07-22

摘要/Abstract

摘要：

有序回归是一种重要的机器学习范式，其目标是针对输出离散且有序的数据建立一个回归器以预测相应有序输出或离散类标。尽管现有的有序回归方法通过利用此类先验有序信息获得了比

一般方法更优的性能。但是，并没有考虑缓变学习准则与有序回归的结合。本文通过缓变学习准则对每个样本类构建多个类内时间序列计算缓变类内散度矩阵，然后在有序约束条件的基础上根据线性判

别准则寻找最佳投影进行有序映射,提出一种新的基于缓变特征学习的判别有序回归方法（Slow Feature Learning Discriminant for Ordinal Regression,SFLDOR）。通过在8个标准有序回归数据集上

的对比实验表明，本算法在回归和分类性能上均优于使用普通类内散度矩阵的算法。

关键词: , 有序回归, 缓变学习准则, 时间序列, 线性判别, 缓变类内散度矩阵

Abstract:

Ordinal regression is an important machine learning paradigm whose purpose is to predict the ordinal outputs or discrete labels by establishing an ordinal

regressor. Many ordinal regression algorithms have been proposed with a better performance by using this prior ordinal information. However, they do not consider the combination

of slowness principle and ordinal regression. This paper first characterizes the slowness within-class scatter matrix with a number of within-class time series, and then

establishes a slow feature learning discriminant for ordinal regression(SFLDOR) by combining the matrix with an ordinal restriction. The experimental results with the eight

standard ordinal regression data sets demonstrate that SFLDOR has a better regression and classification performance than the algorithm with general within-class scatter matrix.

Key words: ordinal regression, slow learning principle, time series, linear discriminant, slowness within-class scatter matrix

李亚克，高航. 基于缓变特征学习的判别有序回归[J]. 计算机与现代化, 2016, 251(07): 24-27,32.

LI Ya-ke, GAO Hang. Slow Feature Learning Discriminant for Ordinal Regression[J]. Computer and Modernization, 2016, 251(07): 24-27,32.

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基于缓变特征学习的判别有序回归

Slow Feature Learning Discriminant for Ordinal Regression

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