• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Multiple Kernel Learning for Dimensionality Reduction
【24h】

Multiple Kernel Learning for Dimensionality Reduction

机译：多核学习以减少维数

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

In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: First, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.
机译：在解决复杂的视觉学习任务时，采用多个描述符来更精确地表征数据已成为提高性能的可行方法。所得的数据表示形式通常是高维的，并且采用多种形式。因此，找到一种将它们转换为较低维度的统一空间的方法通常会简化诸如对象识别或聚类之类的基础任务。为此，所提出的方法（称为MKL-DR）概括了用于降维的多核学习框架，并通过以下三个主要贡献与众不同：首先，我们的方法提供了使用各种图像描述符来描述有用特征的便利性。有关基础数据的各个方面。其次，它扩展了广泛的现有降维技术以考虑多核学习，因此提高了其有效性。第三，通过关注与降维有关的技术，该公式引入了具有多核学习框架的一类新应用程序，不仅解决了有监督的学习问题，还解决了无监督和半监督的问题。

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