Slow Feature Learning Discriminant for Ordinal Regression

doi:10.3969/j.issn.1006-2475.2016.07.005

Abstract

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

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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Slow Feature Learning Discriminant for Ordinal Regression

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