不完备数据的鲁棒多视角图学习及其聚类应用
作者:
作者单位:

1.哈尔滨理工大学;2.哈尔滨工业大学

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Robust multi-view graph learning with applications to clustering for incomplete data
Author:
Affiliation:

Harbin University of Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    现有多视角图学习方法主要建立在数据具有较好完备性的前提假设下,没有充分的考虑由于特征缺失引起的不完备数据的学习问题。面向这一问题,本文提出一种不完备数据的多视角图学习方法。一方面,从局部视角内将数据重建和图学习放入同一框架,通过不完备数据补偿,实现从重建数据中学习视角专属的近邻关系,弥补特征缺失对数据分布的影响。另一方面,为了保持近邻图的二维结构,引入张量分析,从全局角度构造基于多视角的融合图学习约束,捕获缺失数据下视角间图结构的高阶潜在关联性。本文框架交替的优化数据重建、视角专属图学习和融合张量图结构学习,使其在迭代中相互促进,有效提高模型对不完备多视角数据的学习能力。将提出的方法应用于两类不完备数据的多视角聚类实验,其结果表明所提方法在多项性能指标和鲁棒性方面均优于当前主流的多视角图学习方法。

    Abstract:

    The existing multi-view graph learning methods are mainly based on the premise that the data has good completeness, and do not fully consider the learning problem on incomplete data caused by element missing. To address this issue, this paper proposes a multi-view graph learning method with incomplete data. On the one hand, the method puts the data reconstruction and graph learning into the unified framework within view, which learns the view specific neighbor relationship among samples from the reconstructed data to compensate for the influence of data missing on data distribution. On the other hand, in order to preserve the two-dimensional structure of the neighbor graph, tensor analysis is introduced to globally construct a multi-view based fusion graph learning constraint to further capture the high-order potential correlations hidden in multiple views. The proposed framework optimizes the data reconstruction, view-specific graph and fusion graph learning alternatively, which benefits each other during iterations and effectively improve learning ability on incomplete data. The proposed graph learning method is applied to two kinds of incomplete data spectral clustering experiments. The experimental results demonstrate that our proposed method outperforms the existing mainstream multi-view graph learning methods on multiple evaluations and robustness.

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  • 收稿日期:2021-05-12
  • 最后修改日期:2021-08-11
  • 录用日期:2021-08-18
  • 在线发布日期: 2021-09-01
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