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Sparse principal component analysis via regularized low rank matrix approximation

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Cited by:

  1. Lee, Seokho & Huang, Jianhua Z., 2013. "A coordinate descent MM algorithm for fast computation of sparse logistic PCA," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 26-38.
  2. Kohei Adachi & Nickolay T. Trendafilov, 2016. "Sparse principal component analysis subject to prespecified cardinality of loadings," Computational Statistics, Springer, vol. 31(4), pages 1403-1427, December.
  3. Yixuan Qiu & Jing Lei & Kathryn Roeder, 2023. "Gradient-based sparse principal component analysis with extensions to online learning," Biometrika, Biometrika Trust, vol. 110(2), pages 339-360.
  4. Merola, Giovanni Maria & Chen, Gemai, 2019. "Projection sparse principal component analysis: An efficient least squares method," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 366-382.
  5. Carrizosa, Emilio & Guerrero, Vanesa, 2014. "Biobjective sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 151-159.
  6. Bai, Jushan & Ng, Serena, 2019. "Rank regularized estimation of approximate factor models," Journal of Econometrics, Elsevier, vol. 212(1), pages 78-96.
  7. Lars Eldén & Nickolay Trendafilov, 2019. "Semi-sparse PCA," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 164-185, March.
  8. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," PSE Working Papers halshs-02235543, HAL.
  9. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.
  10. Davood Hajinezhad & Qingjiang Shi, 2018. "Alternating direction method of multipliers for a class of nonconvex bilinear optimization: convergence analysis and applications," Journal of Global Optimization, Springer, vol. 70(1), pages 261-288, January.
  11. Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
  12. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.
  13. Li, Gen & Yang, Dan & Nobel, Andrew B. & Shen, Haipeng, 2016. "Supervised singular value decomposition and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 7-17.
  14. Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
  15. Eleonora Arnone & Luca Negri & Ferruccio Panzica & Laura M. Sangalli, 2023. "Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 3510-3521, December.
  16. Maurizio Vichi, 2017. "Disjoint factor analysis with cross-loadings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 563-591, September.
  17. Shaobo Jin & Irini Moustaki & Fan Yang-Wallentin, 2018. "Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 628-649, September.
  18. Juan C. Laria & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2023. "Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models," Statistical Papers, Springer, vol. 64(1), pages 227-253, February.
  19. Phuong T. Vu & Timothy V. Larson & Adam A. Szpiro, 2020. "Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
  20. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
  21. Lee, Seokho & Shin, Hyejin & Billor, Nedret, 2013. "M-type smoothing spline estimators for principal functions," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 89-100.
  22. Lei Wang & Xin Liu & Yin Zhang, 2023. "A communication-efficient and privacy-aware distributed algorithm for sparse PCA," Computational Optimization and Applications, Springer, vol. 85(3), pages 1033-1072, July.
  23. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
  24. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
  25. Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
  26. Rosember Guerra-Urzola & Katrijn Van Deun & Juan C. Vera & Klaas Sijtsma, 2021. "A Guide for Sparse PCA: Model Comparison and Applications," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 893-919, December.
  27. 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.
  28. Li, Gen, 2020. "Generalized Co-clustering Analysis via Regularized Alternating Least Squares," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
  29. Rosember Guerra-Urzola & Niek C. Schipper & Anya Tonne & Klaas Sijtsma & Juan C. Vera & Katrijn Deun, 2023. "Sparsifying the least-squares approach to PCA: comparison of lasso and cardinality constraint," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 269-286, March.
  30. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
  31. Roman A. Jandarov & Lianne A. Sheppard & Paul D. Sampson & Adam A. Szpiro, 2017. "A novel principal component analysis for spatially misaligned multivariate air pollution data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 3-28, January.
  32. Lê Cao Kim-Anh & Rossouw Debra & Robert-Granié Christèle & Besse Philippe, 2008. "A Sparse PLS for Variable Selection when Integrating Omics Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-32, November.
  33. Gautam Sabnis & Debdeep Pati & Anirban Bhattacharya, 2019. "Compressed Covariance Estimation with Automated Dimension Learning," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 466-481, December.
  34. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
  35. D. F. Nwosu & V. U. Ekhosuehi & J. I. Mbegbu, 2020. "Performance of Some Factor Analysis Techniques," Annals of Data Science, Springer, vol. 7(2), pages 209-242, June.
  36. Ningning Xia & Zhidong Bai, 2019. "Convergence rate of eigenvector empirical spectral distribution of large Wigner matrices," Statistical Papers, Springer, vol. 60(3), pages 983-1015, June.
  37. Guerra Urzola, Rosember & Van Deun, Katrijn & Vera, J. C. & Sijtsma, K., 2021. "A guide for sparse PCA : Model comparison and applications," Other publications TiSEM 4d35b931-7f49-444b-b92f-a, Tilburg University, School of Economics and Management.
  38. Amir Beck & Yakov Vaisbourd, 2016. "The Sparse Principal Component Analysis Problem: Optimality Conditions and Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 119-143, July.
  39. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," PSE Working Papers halshs-03626503, HAL.
  40. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
  41. Seong-Yun Hong & Seonggook Moon & Sang-Hyun Chi & Yoon-Jae Cho & Jeon-Young Kang, 2022. "Local Sparse Principal Component Analysis for Exploring the Spatial Distribution of Social Infrastructure," Land, MDPI, vol. 11(11), pages 1-16, November.
  42. Nerea González-García & Ana Belén Nieto-Librero & Purificación Galindo-Villardón, 2023. "CenetBiplot: a new proposal of sparse and orthogonal biplots methods by means of elastic net CSVD," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 5-19, March.
  43. Shen, Dan & Shen, Haipeng & Marron, J.S., 2013. "Consistency of sparse PCA in High Dimension, Low Sample Size contexts," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 317-333.
  44. Johannes Tang Kristensen, 2013. "Diffusion Indexes with Sparse Loadings," CREATES Research Papers 2013-22, Department of Economics and Business Economics, Aarhus University.
  45. Liu, Zhongkai & Song, Rui & Zeng, Donglin & Zhang, Jiajia, 2017. "Principal components adjusted variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 134-144.
  46. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
  47. Peter Bentler & Jan Leeuw, 2011. "Factor Analysis via Components Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 461-470, July.
  48. Jushan Bai & Serena Ng, 2017. "Principal Components and Regularized Estimation of Factor Models," Papers 1708.08137, arXiv.org, revised Nov 2017.
  49. Yiyuan She, 2017. "Selective factor extraction in high dimensions," Biometrika, Biometrika Trust, vol. 104(1), pages 97-110.
  50. Fang, Kuangnan & Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge, 2018. "Integrative sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 1-16.
  51. Mitzi Cubilla-Montilla & Ana Belén Nieto-Librero & M. Purificación Galindo-Villardón & Carlos A. Torres-Cubilla, 2021. "Sparse HJ Biplot: A New Methodology via Elastic Net," Mathematics, MDPI, vol. 9(11), pages 1-15, June.
  52. Shuichi Kawano, 2021. "Sparse principal component regression via singular value decomposition approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 795-823, September.
  53. Nickolay Trendafilov & Kohei Adachi, 2015. "Sparse Versus Simple Structure Loadings," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 776-790, September.
  54. Kehui Chen & Jing Lei, 2015. "Localized Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1266-1275, September.
  55. Qi, Xin & Luo, Ruiyan & Zhao, Hongyu, 2013. "Sparse principal component analysis by choice of norm," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 127-160.
  56. Xin Qi & Ruiyan Luo, 2015. "Sparse Principal Component Analysis in Hilbert Space," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 270-289, March.
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