Chuanquan Li

• PhD
• PhD Student at Central South University

Partial least squares regression (PLS) is a popular multivariate statistical analysis method. It not only can deal with high-dimensional variables but also can effectively select variables. However, the traditional PLS variable selection approaches cannot deal with some prior important variables. In this article, we propose two filter PLS variable...

Biclustering is widely used in different kinds of fields including gene information analysis, text mining, and recommendation system by effectively discovering the local correlation between samples and features. However, many biclustering algorithms will collapse when facing heavy-tailed data. In this paper, we propose a robust version of convex bi...

Multi-sources dataset analysis has been researched for a few years and it aims at improving the potential performance for discoveries by combining data from different sources. Meanwhile, biclustering analysis, widely applied for biology, chemistry, and so on, is a powerful tool that allows the clustering of rows and columns simultaneously. This pap...

Recently, several variants of random forest have been derived for the classification problems, among which the rotation forest is an important type to improve the model’s accuracy. In this article, we proposed a simple and effective variation of rotation forest, which the canonical partial least squares algorithm is employed to rotate the variable...

Two nonparametric feature screening methods, namely, the Kolmogorov filter and model free, marginally measure the relationship between categorical response and predictor variables without the parametrical assumption. And they can select important variables in the high‐dimensional classification data. Random forest, as a classical nonparametric meth...