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Convex biclustering

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  • Eric C. Chi
  • Genevera I. Allen
  • Richard G. Baraniuk

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  • Eric C. Chi & Genevera I. Allen & Richard G. Baraniuk, 2017. "Convex biclustering," Biometrics, The International Biometric Society, vol. 73(1), pages 10-19, March.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:10-19
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    File URL: http://hdl.handle.net/10.1111/biom.12540
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    References listed on IDEAS

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    1. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Turner, Heather & Bailey, Trevor & Krzanowski, Wojtek, 2005. "Improved biclustering of microarray data demonstrated through systematic performance tests," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 235-254, February.
    6. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
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    Cited by:

    1. Chakraborty, Saptarshi & Das, Swagatam, 2021. "On uniform concentration bounds for Bi-clustering by using the Vapnik–Chervonenkis theory," Statistics & Probability Letters, Elsevier, vol. 175(C).
    2. Gong, Tingnan & Zhang, Weiping & Chen, Yu, 2023. "Uncovering block structures in large rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    3. Pi, J. & Wang, Honggang & Pardalos, Panos M., 2021. "A dual reformulation and solution framework for regularized convex clustering problems," European Journal of Operational Research, Elsevier, vol. 290(3), pages 844-856.
    4. Li, Gen, 2020. "Generalized Co-clustering Analysis via Regularized Alternating Least Squares," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

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