• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Discriminative multiview nonnegative matrix factorization with large margin for image classification
【24h】

Discriminative multiview nonnegative matrix factorization with large margin for image classification

机译：具有较大余量的判别式多视图非负矩阵分解

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Image classification has attracted lots of attentions in recent years. To improve classification accuracy, multiple features are usually extracted to represent the context of images, which imposes a challenge for the combination of those features. To address this problem, we present a discriminative nonnegative multi-view learning approach for image classification based on the observation that those features are often nonnegative. For discrimination, we utilize class label as an auxiliary information to learn discriminative common representations through a set of nonnegative basis vectors with large margin. Meanwhile, view consistency constraint is imposed on the low-dimensional representations and correntropy-induced metric (CIM) is adopted for the measurement of reconstruction errors. We utilized half-quadratic optimization technique to solve the optimization problem and obtain an effective multiplicative update rule. Experimental results demonstrate the learned common latent representations by the proposed method are more efficient than other methods.
机译：图像分类近年来引起了很多关注。为了提高分类精度，通常会提取多个特征来表示图像的上下文，这对那些特征的组合提出了挑战。为了解决这个问题，我们提出了一种基于判别性的非负多视图学习方法，该方法基于观察到的那些特征通常是非负的特征。为了进行区分，我们利用类别标签作为辅助信息，通过一组具有较大余量的非负基向量来学习区分性通用表示。同时，对低维表示施加了视图一致性约束，并采用了熵诱导度量（CIM）来度量重建误差。我们利用半二次优化技术来解决优化问题并获得有效的乘法更新规则。实验结果表明，所提方法所学习的共同潜在表示比其他方法更有效。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号