Multiview clustering method for view-unaligned data

Ao LI; Cong FENG; Yutong NIU; Shibiao XU; Yingtao ZHANG; Guanglu SUN

doi:10.11959/j.issn.1000-436x.2022134

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Multiview clustering method for view-unaligned data

Papers | 更新时间：2024-06-05

- Multiview clustering method for view-unaligned data
- Journal on Communications Vol. 43, Issue 7, Pages: 143-152(2022)
- 作者机构：
  
  1. 哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
  2. 北京邮电大学人工智能学院，北京 100876
  3. 哈尔滨工业大学计算机科学与技术学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62071157);University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2018203);The Natural Science Foundation of Heilongjiang Province(YQ2019F011);The Fundamental Research Founds for the University of Heilongjiang Province(LGYC2018JQ013);Postdoctoral Foundation of Heilongjiang Province(LBH-Q19112)
- DOI：10.11959/j.issn.1000-436x.2022134
  CLC： TP391
- Online First：2022-06，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
Ao LI, Cong FENG, Yutong NIU, et al. Multiview clustering method for view-unaligned data[J]. Journal on Communications, 2022, 43(7): 143-152.
DOI：

Ao LI, Cong FENG, Yutong NIU, et al. Multiview clustering method for view-unaligned data[J]. Journal on Communications, 2022, 43(7): 143-152. DOI： 10.11959/j.issn.1000-436x.2022134.

摘要

如何在视角对齐关系错位时有效进行非对齐多视角学习是一类新的挑战性问题。针对这一问题，提出面向视角非对齐数据的多视角聚类方法。一方面，为了捕获多视角异构特征的跨视角相似度信息，基于多视角非负矩阵分解进行表示学习，将原始特征嵌入一个可度量的低维同构空间。另一方面，在低维同构空间中，以二部图最优匹配模型建模视角对齐关系，并提出参考视角概念将模型推广至多视角情形。将表示学习和视角对齐关系学习整合到统一的Bi-level优化框架，使其在迭代中相互促进，进一步提高模型对视角非对齐数据的学习能力。在视角非对齐数据聚类应用上的大量实验结果表明，相比于8种先进的多视角聚类方法，所提方法在3个数据集上的多项性能指标均取得了较优的性能。

Abstract

A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second

view-alignment relationships were modeled as optimal matching of a bipartite graph

which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes

effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics.

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references

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