基于随机旋转集成的降维方法

对随机旋转集成方法提出了一种针对降维问题的改进,得到了新的降维算法框架进行随机变换降维,可以显著减少降维过程中造成的信息损失.采用随机变换降维后,训练监督学习算法时可以获得更高的准确率和更好的泛化性能.通过在模拟数据上进行的实验,证明了使用多重共线性数据进行回归分析时,与传统降维算法相比,经随机变换降维处理后可以保留更多的信息,获得更小的均方误差.对随机变换降维在手写数字识别数据集上的表现进行了研究,证明了与一般性的降维算法相比,随机变换降维在图像分类问题上可以获得更高的准确率.

Dimension Reduction Method Based on the Random Rotation Ensemble

For the random rotation ensemble method,a new dimension reduction algorithm named random transform dimension reduction was proposed,which can reduce the information loss caused by the reduction dimension.After the random transform dimension reduction,the training supervised learning algorithm can obtain higher accuracy and better generalization performance.Through the experiments on the simulated data,it is proved that the regression analysis using multiple collinearity data can retain more information and obtain smaller mean square error than the traditional dimensionality reduction method.The performance of the random transform dimension reduction in handwritten numeral recognition datasets was studied,and it is proved that,compared with the general dimensionality reduction algorithm,the random transform dimension reduction can achieve higher accuracy in image classification.