주성분 분석 (Korean Wikipedia)

Analysis of information sources in references of the Wikipedia article "주성분 분석" in Korean language version.

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7doi.org

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3arxiv.org

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2web.archive.org

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2mathworks.com

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2uta.edu

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1springer.com

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1smmu.edu.cn

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1ieee.org

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1ulb.ac.be

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1wolfram.com

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1gatech.edu

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1linkedin.com

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1ihes.fr

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1curie.fr

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1narod.ru

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1utoronto.ca

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arxiv.org

Jonathon Shlens, A Tutorial on Principal Component Analysis.
Geiger, Bernhard; Kubin, Gernot (Sep 2012). “Relative Information Loss in the PCA”. 《Proc. IEEE Information Theory Workshop》: 562–566.
A. N. Gorban, A. Y. Zinovyev, Principal Graphs and Manifolds, In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Olivas E.S. et al Eds. Information Science Reference, IGI Global: Hershey, PA, USA, 2009. 28–59.

curie.fr

bioinfo-out.curie.fr

A. Zinovyev, ViDaExpert – Multidimensional Data Visualization Tool (free for non-commercial use). Institut Curie, Paris.

doi.org

dx.doi.org

Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space” (PDF). 《Philosophical Magazine》 2 (11): 559–572. doi:10.1080/14786440109462720. 2018년 6월 22일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 29일에 확인함.
Abdi. H., & Williams, L.J. (2010). “Principal component analysis.”. 《Wiley Interdisciplinary Reviews: Computational Statistics,》 2: 433–459. doi:10.1002/wics.101. CS1 관리 - 여러 이름 (링크)
Bengio, Y.; 외. (2013). “Representation Learning: A Review and New Perspectives” (PDF). 《Pattern Analysis and Machine Intelligence》 35 (8). doi:10.1109/TPAMI.2013.50.
Linsker, Ralph (March 1988). “Self-organization in a perceptual network”. 《IEEE Computer》 21 (3): 105–117. doi:10.1109/2.36.
Drineas, P.; A. Frieze; R. Kannan; S. Vempala; V. Vinay (2004). “Clustering large graphs via the singular value decomposition” (PDF). 《Machine learning》 56: 9–33. doi:10.1023/b:mach.0000033113.59016.96. 2012년 8월 2일에 확인함.
Lu, Haiping; Plataniotis, K.N.; Venetsanopoulos, A.N. (2011). “A Survey of Multilinear Subspace Learning for Tensor Data” (PDF). 《Pattern Recognition》 44 (7): 1540–1551. doi:10.1016/j.patcog.2011.01.004.
Kriegel, Hans-Peter (2008). “A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms”. 《Lecture Notes in Computer Science》 5069: 418–435. doi:10.1007/978-3-540-69497-7_27.

gatech.edu

cc.gatech.edu

Drineas, P.; A. Frieze; R. Kannan; S. Vempala; V. Vinay (2004). “Clustering large graphs via the singular value decomposition” (PDF). 《Machine learning》 56: 9–33. doi:10.1023/b:mach.0000033113.59016.96. 2012년 8월 2일에 확인함.

ieee.org

ieeexplore.ieee.org

Bengio, Y.; 외. (2013). “Representation Learning: A Review and New Perspectives” (PDF). 《Pattern Analysis and Machine Intelligence》 35 (8). doi:10.1109/TPAMI.2013.50.

ihes.fr

Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J. et al.: Gene expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer" Lancet 365, 671–679 (2005); Data online

linkedin.com

http://www.linkedin.com/groups/What-is-difference-between-factor-107833.S.162765950

mathworks.com

eig function Matlab documentation
MATLAB PCA-based Face recognition software

narod.ru

pca.narod.ru

A.N. Gorban, B. Kegl, D.C. Wunsch, A. Zinovyev (Eds.), Principal Manifolds for Data Visualisation and Dimension Reduction, LNCSE 58, Springer, Berlin – Heidelberg – New York, 2007. ISBN 978-3-540-73749-0

smmu.edu.cn

stat.smmu.edu.cn

Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space” (PDF). 《Philosophical Magazine》 2 (11): 559–572. doi:10.1080/14786440109462720. 2018년 6월 22일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 29일에 확인함.

springer.com

Jolliffe I.T. Principal Component Analysis Archived 2019년 10월 16일 - 웨이백 머신, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4

ulb.ac.be

A.A. Miranda, Y.-A. Le Borgne, and G. Bontempi. New Routes from Minimal Approximation Error to Principal Components, Volume 27, Number 3 / June, 2008, Neural Processing Letters, Springer

uta.edu

ranger.uta.edu

H. Zha, C. Ding, M. Gu, X. He and H.D. Simon (Dec 2001). “Spectral Relaxation for K-means Clustering” (PDF). 《Neural Information Processing Systems vol.14 (NIPS 2001)》 (Vancouver, Canada): 1057–1064.
Chris Ding and Xiaofeng He (July 2004). “K-means Clustering via Principal Component Analysis” (PDF). 《Proc. of Int'l Conf. Machine Learning (ICML 2004)》: 225–232.

utoronto.ca

dsp.utoronto.ca

Lu, Haiping; Plataniotis, K.N.; Venetsanopoulos, A.N. (2011). “A Survey of Multilinear Subspace Learning for Tensor Data” (PDF). 《Pattern Recognition》 44 (7): 1540–1551. doi:10.1016/j.patcog.2011.01.004.

web.archive.org

Jolliffe I.T. Principal Component Analysis Archived 2019년 10월 16일 - 웨이백 머신, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4
Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space” (PDF). 《Philosophical Magazine》 2 (11): 559–572. doi:10.1080/14786440109462720. 2018년 6월 22일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 29일에 확인함.

wolfram.com

reference.wolfram.com

Eigenvalues function Mathematica documentation