Higher order spectral regression discriminant analysis (HOSRDA): A tensor feature reduction method for ERP detection
作者：
Highlights：
• Higher Order Spectral Regression Analysis (HOSRDA) is a higher order feature reduction method, a multiway extension of Spectral Regression Discriminant Analysis (SRDA). It is used in a classification framework, accompanied by an LDA classifier.
• Despite all present tensor-based feature reduction techniques that solve an eigenvalue problem, HOSRDA solves a regression problem. Accordingly, different types of regularizations can be added to the problem.
• When the number of samples is large, low cost iterative algorithms can be employed to solve the regression problem, while the eigenvalue problem will have a high computational cost.
• The performance of HOSRDA is evaluated on classification of data of a P300 speller from BCI competition III and it reached average character detection accuracy of 96.5% for the two subjects. HOSRDA outperforms almost all of other reported methods on this dataset.
• The bold advantage of HOSRDA over other conventional methods used for classification in P300 Speller is its training time, which is significantly small (for example in comparison to eSVM).
摘要
•Higher Order Spectral Regression Analysis (HOSRDA) is a higher order feature reduction method, a multiway extension of Spectral Regression Discriminant Analysis (SRDA). It is used in a classification framework, accompanied by an LDA classifier.•Despite all present tensor-based feature reduction techniques that solve an eigenvalue problem, HOSRDA solves a regression problem. Accordingly, different types of regularizations can be added to the problem.•When the number of samples is large, low cost iterative algorithms can be employed to solve the regression problem, while the eigenvalue problem will have a high computational cost.•The performance of HOSRDA is evaluated on classification of data of a P300 speller from BCI competition III and it reached average character detection accuracy of 96.5% for the two subjects. HOSRDA outperforms almost all of other reported methods on this dataset.•The bold advantage of HOSRDA over other conventional methods used for classification in P300 Speller is its training time, which is significantly small (for example in comparison to eSVM).
论文关键词：HOSRDA,Tensor decomposition,Tucker decomposition,P300 speller,BCI,SRDA,LDA,HODA
论文评审过程：Received 27 October 2016, Revised 17 March 2017, Accepted 7 May 2017, Available online 8 May 2017, Version of Record 18 May 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.05.004
