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Items: 1 to 20 of 84

1.

On Consistency and Sparsity for Principal Components Analysis in High Dimensions.

Johnstone IM, Lu AY.

J Am Stat Assoc. 2009 Jun 1;104(486):682-693.

2.

Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data.

Sill M, Saadati M, Benner A.

Bioinformatics. 2015 Aug 15;31(16):2683-90. doi: 10.1093/bioinformatics/btv197. Epub 2015 Apr 10.

3.

Stochastic convex sparse principal component analysis.

Baytas IM, Lin K, Wang F, Jain AK, Zhou J.

EURASIP J Bioinform Syst Biol. 2016 Sep 9;2016(1):15. eCollection 2016 Dec.

4.

Sparse Principal Component Analysis via Rotation and Truncation.

Hu Z, Pan G, Wang Y, Wu Z.

IEEE Trans Neural Netw Learn Syst. 2015 Dec 22. doi: 10.1109/TNNLS.2015.2427451. [Epub ahead of print]

PMID:
28055907
5.

Sparse Principal Component Analysis via Rotation and Truncation.

Hu Z, Pan G, Wang Y, Wu Z.

IEEE Trans Neural Netw Learn Syst. 2016 Apr;27(4):875-90. doi: 10.1109/TNNLS.2015.2427451. Epub 2015 Dec 22.

PMID:
26841416
6.

Simple exponential family PCA.

Li J, Tao D.

IEEE Trans Neural Netw Learn Syst. 2013 Mar;24(3):485-97. doi: 10.1109/TNNLS.2012.2234134.

PMID:
24808320
7.

SPARSE LOGISTIC PRINCIPAL COMPONENTS ANALYSIS FOR BINARY DATA.

Lee S, Huang JZ, Hu J.

Ann Appl Stat. 2010 Sep 1;4(3):1579-1601.

8.

Incorporating biological information in sparse principal component analysis with application to genomic data.

Li Z, Safo SE, Long Q.

BMC Bioinformatics. 2017 Jul 11;18(1):332. doi: 10.1186/s12859-017-1740-7.

9.

Robust 2D principal component analysis: a structured sparsity regularized approach.

Yipeng Sun, Xiaoming Tao, Yang Li, Jianhua Lu.

IEEE Trans Image Process. 2015 Aug;24(8):2515-26. doi: 10.1109/TIP.2015.2419075. Epub 2015 Apr 1.

PMID:
25838521
10.

Principal component analysis based methods in bioinformatics studies.

Ma S, Dai Y.

Brief Bioinform. 2011 Nov;12(6):714-22. doi: 10.1093/bib/bbq090. Epub 2011 Jan 17.

11.

Principal Component Analysis With Sparse Fused Loadings.

Guo J, James G, Levina E, Michailidis G, Zhu J.

J Comput Graph Stat. 2010;19(4):930-946.

12.

Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

Khan Z, Shafait F, Mian A.

IEEE Trans Image Process. 2015 Dec;24(12):4934-42. doi: 10.1109/TIP.2015.2472280. Epub 2015 Aug 24.

PMID:
26316126
13.

Sparse Exponential Family Principal Component Analysis.

Lu M, Huang JZ, Qian X.

Pattern Recognit. 2016 Dec;60:681-691. doi: 10.1016/j.patcog.2016.05.024. Epub 2016 May 21.

PMID:
28066030
14.

Structured Sparse Principal Components Analysis with the TV-Elastic Net Penalty.

de Pierrefeu A, Lofstedt T, Hadj-Selem F, Dubois M, Jardri R, Fovet T, Ciuciu P, Frouin V, Duchesnay E.

IEEE Trans Med Imaging. 2017 Sep 4. doi: 10.1109/TMI.2017.2749140. [Epub ahead of print]

PMID:
28880163
15.

Accounting for probe-level noise in principal component analysis of microarray data.

Sanguinetti G, Milo M, Rattray M, Lawrence ND.

Bioinformatics. 2005 Oct 1;21(19):3748-54. Epub 2005 Aug 9.

PMID:
16091409
16.

Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data.

Jiang Q, Shi J.

Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3366-9. doi: 10.1109/EMBC.2014.6944344.

PMID:
25570712
17.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

Li XL, Yi SL, He SL, Lü Q, Xie RJ, Zheng YQ, Deng L.

Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Sep;35(9):2639-43. Chinese.

PMID:
26669182
18.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million.

Fisher A, Caffo B, Schwartz B, Zipunnikov V.

J Am Stat Assoc. 2016;111(514):846-860. Epub 2016 Aug 18.

19.

[Tensor Feature Extraction Using Multi-linear Principal Component Analysis for Brain Computer Interface].

Wang J, Yang L.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Jun;32(3):526-30. Chinese.

PMID:
26485972
20.

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