不对称和离散的自我表示增强哈希用于跨域检索
概括
本研究引入了非对称和离散的自我表示增强哈希 (ADSEH) 来改善跨域检索. ADSEH通过学习自我表达来提高哈希代码质量和概括性,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 机器学习 机器学习
背景情况:
- 基于哈希的检索提供了效率,但在跨域数据方面存在困难.
- 当检索和查询集在域内不同时,现有的方法通常会失败.
- 跨模式数据中的自我表示通常被忽视.
研究的目的:
- 为跨域检索提出一个有效的散列方法.
- 为了提高跨多个数据域的基于散列检索的性能.
- 解决现有方法在处理异质语义差距方面的局限性.
主要方法:
- 建议使用非对称和离散的自我表示增强哈希 (ADSEH).
- 域调整调整数学分布以最大限度地减少不匹配.
- 自我表达是学习的,并嵌入到哈希代码中以提高语义相关性和概括性.
- 日志概率相似性保留了跨域数据关系.
主要成果:
- 与最先进的基线方法相比,ADSEH显示出更高的性能.
- 该方法有效地减少了跨域检索中的异质语义差距.
- 实现了增强的语义相关性和哈希代码质量.
结论:
- ADSEH为跨域检索任务提供了一个强大的解决方案.
- 自我代表的整合大大提高了检索概括性.
- 拟议的方法推进了基于哈希的信息检索领域.
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