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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data
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Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data

机译：用于多视图数据的共同学习非负相关和不相关的功能

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摘要

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.
机译：多视图数据可以表示来自不同视角的对象，从而提供数据分析的互补信息。在多视图学习中非常重要的主题是找到低维潜在子空间，其中常见的语义特征是由多个数据集共享的。然而，大多数现有方法忽略不相关的项目（即，查看特定功能），并且可能导致常见特征学习过程中的语义偏见。在本文中，我们提出了一个非负相关和不相关的特征协同学习（COUFC）方法来解决这一问题。更具体地，当在潜在语义子空间中的视图中学习常见的（相关的）特征时，为每个视图识别特定于视图的（不相关的）特征。通过消除不相关信息的效果，可以捕获有用的视图间特征相关性。我们在COUFC中设计了新的目标函数，并得出了一种优化方法来解决其收敛分析的目标。真实世界传感器，图像和文本数据集的实验表明，所提出的方法优于最先进的多视图学习方法。

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  • 作者

    Zhao Liang; Yang Tao; Zhang Jie; Chen Zhikui; Yang Yi; Wang Z. Jane;

  • 作者单位

    Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China|Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China;

    Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China|Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China;

    Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China|Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China;

    Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China|Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China;

    Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China;

    Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Optimization; Encoding; Learning systems; Correlation; Transforms; Data models; Semantics; Co-learning; correlated features; multi-view data; uncorrelated features;

    机译：优化;编码;学习系统;相关;转换;数据模型;语义;共同学习;相关特征;多视图数据;不相关的功能;不相关的功能;

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