利用正则化矩阵分解技术的多视图聚类方法  

Regularized Matrix Factorization Algorithm for Multi-View Data Clustering

作　　者：徐霜 余琍[2] XU Shuang;YU Li(Department of Hi-tech Industry,Wuhan University,Wuhan 430072,China;School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学高新技术产业发展部,武汉430072 [2]武汉大学计算机学院,武汉430072

出　　处：《计算机工程与应用》2019年第14期142-147,161,共7页Computer Engineering and Applications

基　　金：湖北省自然科学基金(No.2018CFB432)

摘　　要：为了解决具有多种特征属性的多媒体数据(多视图数据)挖掘问题,在非负矩阵分解(NMF)算法的基础上,提出了一种多视图正则化矩阵分解算法(MRMF),该算法使用了多元非负矩阵分解技术,同时使用 L2,1 范数描述矩阵分解的损失函数,并采用多视图流形正则化对矩阵分解进行正则化约束。与现有的一些数据聚类或多视图聚类算法相比,提出的MRMF算法不易受到原始数据中噪声的影响,而且能够充分考虑到不同视图在聚类中所具有不同权重的问题,能够对多视图数据进行较为准确的聚类。MRMF算法的有效性在一些经典的公开数据集上进行了验证,并取得了较好的聚类精度。To address the multi-view data clustering for the multi-media data with various of feature representations as input, this paper proposes a Multi-view Regularized Matrix Factorization(MRMF)algorithm based on the well-known Nonnegative Matrix Factorization(NMF). In detail, the proposed MRMF extends the conventional NMF to its multi-view version which considers multiple matrices as input, and then introduces the L2,1 norm to measure the loss of matrix approxi- mation. Furthermore, the multi-view manifold regularization is also considered to regularize the proposed matrix factori- zation. Compared with some existing data clustering as well as multi-view clustering algorithms, the proposed MRMF is less sensitive to noises in the original data and can also better balance the importance of each view by maintaining a set of learnable weights for each view in the manifold regularization. Encouraging experimental results on numerous public multi-view datasets demonstrate the superiority of the model compared to some state-of-the-art methods.

关 键 词：非负矩阵分解 多视图学习 数据聚类 流形正则化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]