Results 241 to 250 of about 232,203 (264)
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Multilinear Sparse Principal Component Analysis
IEEE Transactions on Neural Networks and Learning Systems, 2014In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA).
Zhihui, Lai +4 more
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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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Fuzzy Sparse Deviation Regularized Robust Principal Component Analysis
IEEE Transactions on Image Processing, 2022Robust principal component analysis (RPCA) is a technique that aims to make principal component analysis (PCA) robust to noise samples. The current modeling approaches of RPCA were proposed by analyzing the prior distribution of the reconstruction error terms.
Yunlong Gao +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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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 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 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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Adaptive Weighted Sparse Principal Component Analysis
2018 IEEE International Conference on Multimedia and Expo (ICME), 2018In this paper, we propose an unsupervised feature selection method from the perspective of optimal reconstruction. The features selected by the proposed method can well represent the original data, and the effectiveness of the selected features is demonstrated by robust reconstruction and clustering.
Shuangyan Yi +3 more
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