Comparative research on methods of dimensionality reduction in high-dimension medical data

Big Correlation(BC) is a method of retaining effective information and the correlation coefficient between evaluation target and each extracted feature is maximized. According to the experiment results of LLE and PCA in nonlinear not dense dataset with noise, combined with the idea of BC, improved PCA with the biggest correlation is proposed called Big Correlation PCA(BC-PCA). By extracting the principal components that have the biggest correlation coefficient with target of evaluation, BC-PCA algorithm reduces the dimension. Examples of the three algorithms are given on the front dataset, experimental results show the effectiveness of the three algorithms, concluded that LLE algorithm is not absolutely better than linear method for nonlinear dataset.