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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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A Selective Overview of Sparse Principal Component Analysis
Proceedings of the IEEE, 2018 Principal component analysis (PCA) is a widely used technique for dimension reduction, data processing, and feature extraction. The three tasks are particularly useful and important in high-dimensional data analysis and statistical learning. However, the regular PCA encounters great fundamental challenges under high dimensionality and may produce ...
Lingzhou Xue, Hui Zou
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Weighted Sparse Principal Component Analysis
Chemometrics and Intelligent Laboratory Systems, 2019 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 been proposed that allows to assign elementwise weights to the matrix of residuals, although this may have several useful applications.
Katrijn van Deun +2 more
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Sparse Principal Component Analysis via Variable Projection [PDF]
SIAM Journal on Applied Mathematics, 2020 Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales.
Krithika Manohar +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.
Qian Zhao 0002, Deyu Meng, Zongben Xu
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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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Joint sparse principal component analysis
Pattern Recognition, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuangyan Yi +4 more
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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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A Simple Review of Sparse Principal Components Analysis
Lecture Notes in Computer Science, 2016Principal Component Analysis (PCA) is a common tool for dimensionality reduction and feature extraction, which has been applied in many fields, such as biology, medicine, machine learning and bioinformatics. But PCA has two obvious drawbacks: each principal component is line combination and loadings are non-zero which is hard to interpret.
Jin-Xing Liu +2 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.
Inge Koch +2 more
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