针对Fisher判别法判别同族群体多中心数据准确率低的问题,提出了线性映射模型下的重编码判别分析算法,将Fisher判别法中的降维思想与重编码方法相结合,采用蒙特卡洛法,通过对伪预测数据的划分。以实映射识别率为目标,确定线性判别函数...针对Fisher判别法判别同族群体多中心数据准确率低的问题,提出了线性映射模型下的重编码判别分析算法,将Fisher判别法中的降维思想与重编码方法相结合,采用蒙特卡洛法,通过对伪预测数据的划分。以实映射识别率为目标,确定线性判别函数的待定系数和伪预测数据的划分。实证表明,该算法具有较高的识别率和稳定性。Aiming at the problem of low accuracy of Fisher discriminant method in judging the multicenter data of the same population, a recoding discriminant analysis algorithm under linear mapping model is proposed, which combines the idea of dimension reduction in Fisher discriminant method with recoding method, adopts Monte Carlo method, and divides the pseudo-predicted data. Aiming at the recognition rate of real mapping, the undetermined coefficients of linear discriminant function and the division of false prediction data are determined. The empirical results show that the algorithm has high recognition rate and stability.展开更多
文摘针对Fisher判别法判别同族群体多中心数据准确率低的问题,提出了线性映射模型下的重编码判别分析算法,将Fisher判别法中的降维思想与重编码方法相结合,采用蒙特卡洛法,通过对伪预测数据的划分。以实映射识别率为目标,确定线性判别函数的待定系数和伪预测数据的划分。实证表明,该算法具有较高的识别率和稳定性。Aiming at the problem of low accuracy of Fisher discriminant method in judging the multicenter data of the same population, a recoding discriminant analysis algorithm under linear mapping model is proposed, which combines the idea of dimension reduction in Fisher discriminant method with recoding method, adopts Monte Carlo method, and divides the pseudo-predicted data. Aiming at the recognition rate of real mapping, the undetermined coefficients of linear discriminant function and the division of false prediction data are determined. The empirical results show that the algorithm has high recognition rate and stability.
