CAJ | 학술논문

为增强张量子空间学习的稳定性、可重现性,及其分类能力,提出了一种鲁棒的子空间学习方法--RSOTP.该方法以MFA为鉴别准则,采用稳定的初始化向量和交替形式的正交来去除了权重因子,从而使整个学习过程不仅具有鲁棒性、可重现性,而且具有较强的分类能力,该方法在大小型人脸数据库上进行的实验均取得了较高的识别率.
위증강장양자공간학습적은정성、가중현성,급기분류능력,제출료일충로봉적자공간학습방법--RSOTP.해방법이MFA위감별준칙,채용은정적초시화향량화교체형식적정교래거제료권중인자,종이사정개학 습과정불부구유로봉성、가중현성,이차구유교강적분류능력,해방법재대소형인검수거고상진행적실험균취득료교고적식별솔.
In this paper we proposed a MFA based RSOTP subspace learning method. By employing stable discriminant vector in initialization and using an alternative orthogonal selection and throwing off weight matrix, this approach enhances the robustness of the learning process and strengthens the classification ability, and achieves better recognition performance on both large and small face datasets than conventional techniques.