用哈希分块加速单细胞数据异常检测，提升效率与精度
Neighborhood density estimation using space-partitioning based hashing schemes
- 基于空间分块哈希构建轻量级数据摘要
- 在真实数据上比现有方法快2倍以上，准确率超90%
- 适合处理大规模流式数据的实时监测场景
本文提出FiRE/FiRE.1，一种基于数据摘要的新型算法，用于快速识别大规模单细胞RNA测序数据中的稀有细胞亚群。该方法在多个真实数据集上表现出优于当前最优技术的性能。此外，论文还提出Enhash，一种利用投影哈希实现快速且资源高效的集成学习器，可有效检测流数据中的概念漂移，在不同类型的漂移下均展现出优异的时间效率和准确性。
原文摘要 · Abstract (English)
This work introduces FiRE/FiRE.1, a novel sketching-based algorithm for anomaly detection to quickly identify rare cell sub-populations in large-scale single-cell RNA sequencing data. This method demonstrated superior performance against state-of-the-art techniques. Furthermore, the thesis proposes Enhash, a fast and resource-efficient ensemble learner that uses projection hashing to detect concept drift in streaming data, proving highly competitive in time and accuracy across various drift types.
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