Non-integer Norm Regularized Logistic Regression
In the task of learning from gene expression profiling using microarray techniques,microarray gene expression data is usually highly contaminated by various noises and contain the overwhelming number of genes relative to the number of available samples.Hence,finding efficient and discriminative genes is crucial to cancer classification.However,the problem is NP-complete and brings out a great challenge for machine learning and statistic techniques.In this paper,a class of sparse q-norm regularization terms(0<q≤2)are considered for the developing sparse classifiers for determining discriminative genes,and classification algorithm of conjugate gradient q-norm regularization Logistic regression is proposed.This approach was tried on synthetic datasets and bladder,lymphoma and colon benchmark data sets.It obtained encouraging results on those data sets as compared with L1-norm and L2-norm Logistic regression.
Logistic regression feature selection non-integer norm regularization sparse model
Liu Jianwei Sun Zhengkang Luo Xionglin
Research Institute of Automation China University of Petroleum,P.O.Box 260,18 Fuxue Road,Changping,Beijing,China
国际会议
长沙
英文
1333-1338
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）