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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.03187 |
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| _version_ | 1866909941524070400 |
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| author | Jindal, Aashi |
| author_facet | Jindal, Aashi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neighborhood density estimation using space-partitioning based hashing schemes Jindal, Aashi Machine Learning 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. |
| title | Neighborhood density estimation using space-partitioning based hashing schemes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.03187 |