Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

基于自适应大图的多视图无监督的缩小维度.

Qianyao Qiang1, Bin Zhang2, Chen Jason Zhang1

  • 1Department of Computing, Hong Kong Polytechnic University, 999077, Hong Kong, China.

Neural networks : the official journal of the International Neural Network Society
|March 30, 2025
PubMed
概括
此摘要是机器生成的。

本研究为未标记的数据引入了基于Bigraph的自适应式多视图无监督维度减少 (BMUDR). BMUDR通过自适应地构建图形和权衡各种数据视图以提高效率和性能来增强表示学习.

关键词:
适应式图表是适应性的图表.两分位的图形图表.嵌入式 嵌入式 嵌入式多视图缩小维度的减少没有监督的学习学习.

相关实验视频

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Identification of small-molecule HSF1 amplifiers by high content screening in protection of cells from stress induced injury.

Biochemical and biophysical research communications·2009
Same author

Nanowire transformation by size-dependent cation exchange reactions.

Nano letters·2009
Same author

Effect of haishengsu as an adjunct therapy for patients with advanced renal cell cancer: a randomized and placebo-controlled clinical trial.

Journal of alternative and complementary medicine (New York, N.Y.)·2009
Same author

Identification of inhibitors of HSF1 functional activity by high-content target-based screening.

Journal of biomolecular screening·2009
Same author

Antitumor effects of targeting hTERT lentivirus-mediated RNA interference against KB cell lines.

Oncology research·2009
Same author

Characteristics of emissive spectrum and the removal of nitric oxide in N2/02/NO plasma with argon additive.

Journal of environmental sciences (China)·2009
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 计算机视觉 计算机视觉

背景情况:

  • 基于图形的多视图无监督缩小维度对于未标记的数据至关重要.
  • 现有的方法在异质数据集成,刚性图形和计算需求方面扎.

研究的目的:

  • 提出一种基于Bigraph的多视图无监督缩小尺寸 (BMUDR) 方法.
  • 解决整合多个观点和学习低维表示的挑战.

主要方法:

  • BMUDR动态学习视图特定的集,并构建一个自适应的大图.
  • 它将基生成和相似性矩阵构建集成到维度减少中.
  • 一个优化算法提高了计算效率和可扩展性.

主要成果:

  • BMUDR通过探索样本- anchor关系来有效地学习低维表示.
  • 该方法以适应方式权衡各种视图贡献,利用互补和一致的属性.
  • 广泛的实验证明了对基准数据集的令人印象深刻的性能.

结论:

  • BMUDR提供了一个强大的解决方案,用于多视图无监督的尺寸缩小.
  • 适应式大图方法改善了异质数据的集成和表示.
  • 该方法显示了增强涉及未标记多视图数据的机器学习任务的巨大潜力.