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Pattern Recognition, ISSN 0031-3203, 01/2017, Volume 61, pp. 524 - 536
Principal component analysis (PCA) is widely used in dimensionality reduction. A lot of variants of PCA have been proposed to improve the robustness of the... 
Dimensionality reduction | Joint sparse | [formula omitted]-norm | norm | l(2,1)-norm | FACE RECOGNITION | FRAMEWORK | DICTIONARY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Continuing education | Algorithms | Analysis | Mathematical optimization
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 12/2011, Volume 20, Issue 12, pp. 3419 - 3430
Journal Article
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, ISSN 1364-503X, 04/2016, Volume 374, Issue 2065, p. 20150202
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality... 
Dimension reduction | Eigenvectors | Multivariate analysis | Palaeontology | SPARSE PCA | MULTIDISCIPLINARY SCIENCES | eigenvectors | VARIABLES | palaeontology | dimension reduction | multivariate analysis | 1008 | 144 | 1005 | Review | 175
Journal Article
Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, 12/2019, Volume 195, p. 103875
Sparse principal component analysis (SPCA) has been shown to be a fruitful method for the analysis of high-dimensional data. So far, however, no method has... 
Sparse principal component analysis | Elementwise weighted least squares | Multiplicative-additive error
Journal Article
Biometrics, ISSN 0006-341X, 09/2016, Volume 72, Issue 3, pp. 846 - 854
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 02/2013, Volume 22, Issue 2, pp. 687 - 699
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 2008, Volume 9, pp. 1269 - 1294
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining... 
Sparse recovery | Sparse eigenvalues | Subset selection | Lasso | PCA | ROTATION | subset selection | lasso | APPROXIMATIONS | sparse eigenvalues | sparse recovery | SELECTION | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 02/2010, Volume 11, pp. 517 - 553
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations... 
Block algorithms | Sparse PCA | Strongly convex sets | Gradient ascent | Power method | BREAST-CANCER | SURVIVAL | GENE-EXPRESSION SIGNATURE | strongly convex sets | power method | gradient ascent | sparse PCA | block algorithms | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 4/2019, pp. 1 - 13
Robust principal component analysis (RPCA) can recover low-rank matrices when they are corrupted by sparse noises. In practice, many matrices are, however, of... 
robust principal component analysis (RPCA) | low rank | High rank | Minimization | Sparse matrices | Matrix decomposition | Optimization | subspace clustering | sparse | Feature extraction | Kernel | noise removal | Principal component analysis
Journal Article
Statistics and Computing, ISSN 0960-3174, 5/2018, Volume 28, Issue 3, pp. 713 - 723
Journal Article
Pattern Recognition, ISSN 0031-3203, 2010, Volume 43, Issue 4, pp. 1531 - 1549
This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation... 
Edge preservation | Principal component analysis (PCA) | Denoising | WAVELET DOMAIN | SPARSE | TRANSFORM | SCALE | DICTIONARIES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Liquefied petroleum gas | Analysis | Algorithms
Journal Article
Journal of Multivariate Analysis, ISSN 0047-259X, 07/2018, Volume 166, pp. 1 - 16
In the analysis of data with high-dimensional covariates and small sample sizes, dimension reduction techniques have been extensively employed. Principal... 
Integrative analysis | Sparse PCA | 62H25 | Contrasted penalization | CONSISTENCY | PENALIZATION | STATISTICS & PROBABILITY | MODEL | CANCER | HIGH DIMENSION | PCA
Journal Article
Pattern Recognition Letters, ISSN 0167-8655, 07/2013, Volume 34, Issue 9, pp. 1001 - 1008
Multifocus image fusion has emerged as a major topic in computer vision and image processing community since the optical lenses for most widely used imaging... 
Sparse features | Low-rank matrix | Robust principal component analysis | Multifocus image fusion | 3D SHAPE | PERFORMANCE | FOCUS | ALGORITHM | WAVELET TRANSFORM | NOISE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Annals of Statistics, ISSN 0090-5364, 02/2016, Volume 44, Issue 1, pp. 219 - 254
Journal Article
Journal Article
Journal of the American Statistical Association, ISSN 0162-1459, 07/2015, Volume 110, Issue 511, pp. 1100 - 1111
Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These... 
Sparse data | Robust estimation | Functional data analysis | DISTRIBUTIONS | RANK APPROXIMATION | PROJECTION-PURSUIT APPROACH | OUTLIER | DISPERSION MATRICES | FAST ALGORITHM | STATISTICS & PROBABILITY | ROBUST PCA | COVARIANCE
Journal Article
Proceedings of the IEEE, ISSN 0018-9219, 08/2018, Volume 106, Issue 8, pp. 1311 - 1320
Journal Article
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