Table of Contents
Advances in Artificial Intelligence
Volume 2017, Article ID 1736389, 9 pages
Research Article

Method for Solving LASSO Problem Based on Multidimensional Weight

College of Computer & Information Science, Southwest University, Chongqing, China

Correspondence should be addressed to Chen ShanXiong; moc.361@lmpxsc

Received 15 November 2016; Revised 12 February 2017; Accepted 21 March 2017; Published 4 May 2017

Academic Editor: Farouk Yalaoui

Copyright © 2017 Chen ChunRong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the data mining, the analysis of high-dimensional data is a critical but thorny research topic. The LASSO (least absolute shrinkage and selection operator) algorithm avoids the limitations, which generally employ stepwise regression with information criteria to choose the optimal model, existing in traditional methods. The improved-LARS (Least Angle Regression) algorithm solves the LASSO effectively. This paper presents an improved-LARS algorithm, which is constructed on the basis of multidimensional weight and intends to solve the problems in LASSO. Specifically, in order to distinguish the impact of each variable in the regression, we have separately introduced part of principal component analysis (Part_PCA), Independent Weight evaluation, and CRITIC, into our proposal. We have explored that these methods supported by our proposal change the regression track by weighted every individual, to optimize the approach direction, as well as the approach variable selection. As a consequence, our proposed algorithm can yield better results in the promise direction. Furthermore, we have illustrated the excellent property of LARS algorithm based on multidimensional weight by the Pima Indians Diabetes. The experiment results show an attractive performance improvement resulting from the proposed method, compared with the improved-LARS, when they are subjected to the same threshold value.