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Joint sparse principal component analysis
Pattern Recognition, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi, Shuangyan +4 more
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Sparse Principal Component Analysis With Preserved Sparsity Pattern
IEEE Transactions on Image Processing, 2019Principal component analysis (PCA) is widely used for feature extraction and dimension reduction in pattern recognition and data analysis. Despite its popularity, the reduced dimension obtained from the PCA is difficult to interpret due to the dense structure of principal loading vectors.
Abd-Krim Seghouane +2 more
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Automatic sparse principal component analysis
Canadian Journal of Statistics, 2020The wide availability of computers enables us to accumulate a huge amount of data, thus effective tools to extract information from the huge volume of data have become critical. Principal component analysis (PCA) is a useful and traditional tool for dimensionality reduction of massive high‐dimensional datasets. Recently, sparse principal component (PC)
Heewon Park +3 more
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Sparse Component Analysis Methods
2020In this chapter and the associated appendix present a third class of blind source separation and blind mixture identification methods intended for linear-quadratic mixtures (including their bilinear and purely quadratic restricted versions), namely methods based on sparse component analysis.
Yannick Deville +2 more
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Robust sparse principal component analysis
Science China Information Sciences, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Qian, Meng, DeYu, Xu, ZongBen
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Integrative sparse principal component analysis
Journal of Multivariate Analysis, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kuangnan Fang +3 more
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Sparse Principal Component Analysis in Hilbert Space
Scandinavian Journal of Statistics, 2014AbstractTechnical advances in many areas have produced more complicated high‐dimensional data sets than the usual high‐dimensional data matrix, such as the fMRI data collected in a period for independent trials, or expression levels of genes measured in different tissues.
Qi, Xin, Luo, Ruiyan
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Sparse Principal Component Analysis with Constraints
Proceedings of the AAAI Conference on Artificial Intelligence, 2021The sparse principal component analysis is a variant of the classical principal component analysis, which finds linear combinations of a small number of features that maximize variance across data. In this paper we propose a methodology for adding two general types of feature grouping constraints into the original sparse PCA ...
Mihajlo Grbovic +2 more
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Sparse Discriminant Principle Component Analysis
2018Sparse Principal Component Analysis (SPCA) is a regression-type optimization problem based on PCA. The main advantage of SPCA is that it can get modified PCs with sparse loadings so as to improve the performance of feature extraction. However, SPCA does not consider the label information of the data, which degrades its performance in some practical ...
Zhihui Lai +4 more
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Quadratic Independent Component Analysis Based on Sparse Component
Applied Mechanics and Materials, 2013In this paper, a novel signal blind separation using adaptive multi-resolution independent component analysis based on sparse component is presented. This method separates mixed signal based on quadratic function and sparse representation. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other.
Jing Hui Wang, Shu Gang Tang
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