聚类分析
非负矩阵分解
计算机科学
矩阵分解
基础(线性代数)
人工智能
数据挖掘
基质(化学分析)
规范(哲学)
共识聚类
特征(语言学)
模式识别(心理学)
机器学习
模糊聚类
数学
树冠聚类算法
特征向量
物理
量子力学
哲学
语言学
复合材料
政治学
材料科学
法学
几何学
作者
Menglei Hu,Songcan Chen
标识
DOI:10.24963/ijcai.2018/313
摘要
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.
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