Distance-based outlier detection for high dimension, low sample size data-Reference-Cited by-同舟云学术

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Distance-based outlier detection for high dimension, low sample size data

Published:2018-03-24 Issue:1 Volume:46 Page:13-29
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Ahn Jeongyoun¹,Lee Myung Hee²,Lee Jung Ae³

Affiliation:

1. Department of Statistics, University of Georgia, Athens, GA, USA

2. Center for Global Health, Department of Medicine, Weill Cornell Medical College, New York City, NY, USA

3. Agricultural Statistics Laboratory, University of Arkansas, Fayetteville, AR, USA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2018.1452901

Reference23 articles.

1. Clustering high dimension, low sample size data using the maximal data piling distance

2. The maximal data piling direction for discrimination

3. The high-dimension, low-sample-size geometric representation holds under mild conditions

4. Adjustment of systematic microarray data biases

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Test for high-dimensional outliers with principal component analysis;Japanese Journal of Statistics and Data Science;2024-06-04

2. Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification;Journal of the Korean Statistical Society;2024-04-03

3. Feature consistency anomaly detection method based on adversarial training deep autoencoder;2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2022-11-19

4. Cluster-Based Improved Isolation Forest;Entropy;2022-04-27

5. Surrogate Supervision-based Deep Weakly-supervised Anomaly Detection;2021 International Conference on Data Mining Workshops (ICDMW);2021-12

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