硕士生导师
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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
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Robust graph regularized nonnegative matrix factorization for clustering
影响因子：5.406
DOI码：10.1007/s10618-017-0543-9
所属单位：西南交通大学
发表刊物：Data Mining and Knowledge Discovery
刊物所在地：NETHERLANDS
关键字：Nonnegative matrix factorization ; Robust regularization ; 1-norm function ;Clustering
摘要：Nonnegative matrix factorization and its graph regularized extensions have received significant attention in machine learning and data mining. However, existing approaches are sensitive to outliers and noise due to the utilization of the squared loss function in measuring the quality of graph regularization and data reconstruction. In this paper, we present a novel robust graph regularized NMF model (RGNMF) to approximate the data matrix for clustering. Our assumption is that there may exist some entries of the data corrupted arbitrarily, but the corruption is sparse. To address this problem, an error matrix is introduced to capture the sparse corruption. With this sparse outlier matrix, a robust factorization result could be obtained since a much cleaned data could be reconstructed. Moreover, the 1-norm function is used to alleviate the influence of unreliable regularization which is incurred by unexpected graphs. That is, the sparse error matrix alleviates the impact of noise and outliers, and the 1-norm function leads to a faithful regularization since the influence of the unreliable regularization errors can be reduced. Thus, RGNMF is robust to unreliable graphs and noisy data. In order to solve the optimization problem of our method, an iterative updating algorithm is proposed and its convergence is also guaranteed theoretically. Experimental results show that the proposed method consistently outperforms many state-of-the-art methods.
合写作者：Tao Li,李天瑞, Zenglin Xu
第一作者：Shudong Huang
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20174304309982
学科门类：工学
一级学科：计算机科学与技术
期号：32
页面范围：483–503 (2018)
ISSN号：1384-5810
是否译文：否
发表时间：2017-10-23