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Sparse Principal Component Analysis
 Journal of Computational and Graphical Statistics
, 2004
"... Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA su#ers from the fact that each principal component is a linear combination of all the original variables, thus it is often di#cult to interpret the results. We introduce a new method ca ..."
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Cited by 279 (6 self)
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called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We show that PCA can be formulated as a regressiontype optimization problem, then sparse loadings are obtained by imposing the lasso (elastic net) constraint
WITH SPARSE PRINCIPAL COMPONENT ANALYSIS
"... The development of the technology makes it possible to measure large amount of genes expressions simultaneously. Since biological functions are mostly coordinated by multiple genes, called “gene pathway”, it is interesting to identify differential gene pathways which are associated with clinical phe ..."
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phenotype. Principal component analysis has been proposed to identify differential gene pathways in several literatures, while sparse principal component analysis (SPCA) has not drawn any attention. We proposed to use SPCA to identify differential gene pathways. The results show that, comparing to PCA, SPCA
Sparse Principal Component Analysis
 Journal of Computational and Graphical Statistics
, 2004
"... Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA su#ers from the fact that each principal component is a linear combination of all the original variables, thus it is often di#cult to interpret the results. We introduce a new method ca ..."
Abstract

Cited by 1 (0 self)
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called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We show that PCA can be formulated as a regressiontype optimization problem, then sparse loadings are obtained by imposing the lasso (elastic net) constraint
Sparse Principal Component Analysis with Constraints
 PROCEEDINGS OF THE TWENTYSIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2012
"... The 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 in ..."
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Cited by 5 (1 self)
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The 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
Structured Sparse Principal Component Analysis
, 2009
"... We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This structured sparse PCA is based on a structured regularization recently introduced by [1]. While ..."
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Cited by 70 (14 self)
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We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This structured sparse PCA is based on a structured regularization recently introduced by [1]. While
Robust Sparse Principal Component Analysis
"... A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret. The robustness makes the analysis resistant to outlying observations. The p ..."
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Cited by 5 (0 self)
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A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret. The robustness makes the analysis resistant to outlying observations
StructuredSparse Principal ComponentAnalysis
"... WepresentanextensionofsparsePCA,orsparse dictionary learning, where the sparsity patterns ofalldictionaryelementsarestructuredandconstrainedtobelongtoaprespecifiedsetofshapes. This structured sparse PCA is based on a structuredregularizationrecentlyintroducedbyJenatton et al. (2009). While classical ..."
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WepresentanextensionofsparsePCA,orsparse dictionary learning, where the sparsity patterns ofalldictionaryelementsarestructuredandconstrainedtobelongtoaprespecifiedsetofshapes. This structured sparse PCA is based on a structuredregularizationrecentlyintroducedbyJenatton et al. (2009). While
Optimal Solutions for Sparse Principal Component Analysis
"... Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applica ..."
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Cited by 96 (13 self)
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Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array
Sparse Principal Component Analysis in Hyperspectral Change Detection
"... This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bitemporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very sim ..."
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This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bitemporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very
Results 1  10
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586,339