Results 11 to 20 of about 232,203 (264)
Sparse Principal Component Analysis [PDF]
Journal of Computational and Graphical Statistics, 2006 Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results.
Hui Zou +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.
N. Benjamin Erichson +5 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 logistic principal components analysis for binary data [PDF]
The Annals of Applied Statistics, 2010 We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua +2 more
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Sparse Principal Component Analysis via Rotation and Truncation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2016 Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover the data subspace as much as possible. In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in ...
Hu, Zhenfang +3 more
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Demixed Sparse Principal Component Analysis Through Hybrid Structural Regularizers
IEEE Access, 2021 Recently, the sparse representation of multivariate data has gained great popularity in real-world applications like neural activity analysis. Many previous analyses for these data utilize sparse principal component analysis (SPCA) to obtain a sparse ...
Yan Zhang, Haoqing Xu
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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 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
Lu, Meng +2 more
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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Class-Specific Sparse Principal Component Analysis for Visual Classification
IEEE Access, 2020 Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the
Fei Pan +3 more
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