硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
报考该导师研究生的方式
欢迎你报考王红军老师的研究生，报考有以下方式：
1、参加西南交通大学暑期夏令营活动，提交导师意向时，选择王红军老师，你的所有申请信息将发送给王红军老师，老师看到后将和你取得联系，点击此处参加夏令营活动
2、如果你能获得所在学校的推免生资格，欢迎通过推免方式申请王红军老师研究生，可以通过系统的推免生预报名系统提交申请，并选择意向导师为王红军老师，老师看到信息后将和你取得联系，点击此处推免生预报名
3、参加全国硕士研究生统一招生考试报考王红军老师招收的专业和方向，进入复试后提交导师意向时选择王红军老师。
4、如果你有兴趣攻读王红军老师博士研究生，可以通过申请考核或者统一招考等方式报考该导师博士研究生。
Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering
发表刊物：IEEE Transactions on Computational Social Systems
摘要：The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1 -norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.
合写作者：Tianrui Li,Dexian Wang,Shi-Jinn Horng,Rui Liu
第一作者：Ping Deng
论文类型：SCI
通讯作者：Hongjun Wang
文献类型：J
卷号：10
期号：3
页面范围：910-921
ISSN号：2329-924X
是否译文：否
收录刊物：SCI