Results 11 to 20 of about 50,718 (252)
Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA). [PDF]
J Comput Graph Stat, 2022 We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix from the eigenvalues of the full matrix and associated sub-matrices.
Frost HR.
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Robust Sparse Principal Component Analysis [PDF]
SSRN Electronic Journal, 2011 A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret. The robustness makes the analysis resistant to outlying observations.
Christophe Croux +2 more
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Stochastic convex sparse principal component analysis. [PDF]
EURASIP J Bioinform Syst Biol, 2016 Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data.
Baytas IM +4 more
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Sparse principal component analysis by choice of norm. [PDF]
J Multivar Anal, 2013 Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the loadings (coefficients) of different principal components ...
Qi X, Luo R, Zhao H.
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Sparse Generalised Principal Component Analysis [PDF]
Pattern Recognition, 2018 In this paper, we develop a sparse method for unsupervised dimension reduction for data from an exponential-family distribution. Our idea extends previous work on Generalised Principal Component Analysis by adding L1 and SCAD penalties to introduce sparsity.
Luke Smallman +2 more
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Sparse and Functional Principal Components Analysis [PDF]
2019 IEEE Data Science Workshop (DSW), 2019 The published version of this paper incorrectly thanks "Luofeng Luo" instead of "Luofeng Liao" in the ...
Genevera I. Allen, Michael Weylandt
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Meta Sparse Principal Component Analysis
CoRR, 2022 29 pages, 7 ...
Imon Banerjee, Jean Honorio
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Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. [PDF]
PLoS ONE, 2015 Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the
Nan Lin +3 more
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A New Basis for Sparse Principal Component Analysis
Journal of Computational and Graphical Statistics, 2023 50 pages, 10 ...
Fan Chen, Karl Rohe
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Sparse exponential family Principal Component Analysis [PDF]
Pattern Recognition, 2016 We propose a Sparse exponential family Principal Component Analysis (SePCA) method suitable for any type of data following exponential family distributions, to achieve simultaneous dimension reduction and variable selection for better interpretation of the results. Because of the generality of exponential family distributions, the method can be applied
Meng Lu, Jianhua Z. Huang, Xiaoning Qian
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