The Annals of Statistics

Discussion of “Influential feature PCA for high dimensional clustering”

T. Tony Cai and Linjun Zhang

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Article information

Source
Ann. Statist., Volume 44, Number 6 (2016), 2372-2381.

Dates
Received: May 2016
First available in Project Euclid: 23 November 2016

Permanent link to this document
https://projecteuclid.org/euclid.aos/1479891620

Digital Object Identifier
doi:10.1214/16-AOS1423C

Mathematical Reviews number (MathSciNet)
MR3576546

Zentralblatt MATH identifier
1360.62316

Citation

Cai, T. Tony; Zhang, Linjun. Discussion of “Influential feature PCA for high dimensional clustering”. Ann. Statist. 44 (2016), no. 6, 2372--2381. doi:10.1214/16-AOS1423C. https://projecteuclid.org/euclid.aos/1479891620


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References

  • [1] Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis, 3rd ed. Wiley, Hoboken, NJ.
  • [2] Birnbaum, A., Johnstone, I. M., Nadler, B. and Paul, D. (2013). Minimax bounds for sparse PCA with noisy high-dimensional data. Ann. Statist. 41 1055–1084.
  • [3] Cai, T. and Liu, W. (2011). A direct estimation approach to sparse linear discriminant analysis. J. Amer. Statist. Assoc. 106 1566–1577.
  • [4] Cai, T., Ma, Z. and Wu, Y. (2015). Optimal estimation and rank detection for sparse spiked covariance matrices. Probab. Theory Related Fields 161 781–815.
  • [5] Cai, T. T., Ma, Z. and Wu, Y. (2013). Sparse PCA: Optimal rates and adaptive estimation. Ann. Statist. 41 3074–3110.
  • [6] Ma, Z. (2013). Sparse principal component analysis and iterative thresholding. Ann. Statist. 41 772–801.
  • [7] Vershynin, R. (2012). Introduction to the non-asymptotic analysis of random matrices. In Compressed Sensing 210–268. Cambridge Univ. Press, Cambridge, MA.
  • [8] Zou, H., Hastie, T. and Tibshirani, R. (2006). Sparse principal component analysis. J. Comput. Graph. Statist. 15 265–286.

See also

  • Main article: Influential features PCA for high dimensional clustering.