Eigen selection in spectral clustering: a theory guided practice-Reference-Cited by-同舟云学术

Eigen selection in spectral clustering: a theory guided practice

Published:2021-04-16 Issue: Volume: Page:1-33
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Han Xiao¹,Tong Xin²,Fan Yingying²

Affiliation:

1. International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China, 230026;

2. Data Sciences and Operations Department, University of Southern California, Los Angeles, CA, 90089

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.1917418

Reference24 articles.

1. Trevor Hastie, Robert Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition). Springer-Verlag Inc, 2009. ISBN 0-387-95284-5.

2. G. James, D. Witten, T. Hastie, and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Springer New York, 2014. ISBN 9781461471370. URL https://books.google.com.hk/books?id=at1bmAEACAAJ.

3. Paul S Bradley, Usama M Fayyad, and Olvi L Mangasarian. Mathematical programming for data mining: Formulations and challenges. INFORMS Journal on Computing, 11(3):217–238, 1999.

4. Daniela M Witten and Robert Tibshirani. A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490):713–726, 2010.

5. Joe H Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301): 236–244, 1963.

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