Learned k-NN distance estimation-Reference-Cited by-同舟云学术

Learned k-NN distance estimation

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th International Conference on Advances in Geographic Information Systems
language:
Short-container-title:

Author:

Amagata Daichi¹,Arai Yusuke¹,Fujita Sumio²,Hara Takahiro¹

Affiliation:

1. Osaka University, Japan

2. Yahoo Japan Corporation, Japan

Funder

JSPS

JST

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3557915.3560935

Reference14 articles.

1. Daichi Amagata , Yusuke Arai , Sumio Fujita , and Takahiro Hara . 2022. Learned k-NN Distance Estimation. arXiv:2208.14210 ( 2022 ). Daichi Amagata, Yusuke Arai, Sumio Fujita, and Takahiro Hara. 2022. Learned k-NN Distance Estimation. arXiv:2208.14210 (2022).

2. Daichi Amagata and Takahiro Hara. 2021. Fast Density-Peaks Clustering: Multicore-based Parallelization Approach. In SIGMOD. 49--61. Daichi Amagata and Takahiro Hara. 2021. Fast Density-Peaks Clustering: Multicore-based Parallelization Approach. In SIGMOD. 49--61.

3. Daichi Amagata Makoto Onizuka and Takahiro Hara. 2021. Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach. In SIGMOD. 36--48. Daichi Amagata Makoto Onizuka and Takahiro Hara. 2021. Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach. In SIGMOD. 36--48.

4. Daichi Amagata , Makoto Onizuka , and Takahiro Hara . 2022. Fast , exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces. The VLDB Journal ( 2022 ), 1--25. Daichi Amagata, Makoto Onizuka, and Takahiro Hara. 2022. Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces. The VLDB Journal (2022), 1--25.

5. Pierre Baldi , Kyle Cranmer , Taylor Faucett , Peter Sadowski , and Daniel Whiteson . 2016. Parameterized machine learning for high-energy physics. arXiv preprint arXiv:1601.07913 ( 2016 ). Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson. 2016. Parameterized machine learning for high-energy physics. arXiv preprint arXiv:1601.07913 (2016).

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