A DIRECT FORMULATION FOR SPARSE PCA USING SEMIDEFINITE PROGRAMMING∗ ALEXANDRE D’ASPREMONT† , LAURENT EL GHAOUI‡ , MICHAEL I. JORDAN§ , AND GERT R. G. LANCKRIET¶ Abstract. Given a covariance matrix, we consider

Source URL: eceweb.ucsd.edu

• Mathematical optimization
• Matrix theory
• Multivariate statistics
• Linear programming
• Operations research
• Sparse PCA
• Semidefinite programming
• Principal component analysis
• Convex optimization
• Eigenvalues and eigenvectors
• Matrix
• Singular value decomposition

Computation-Risk Tradeoffs for Covariance-Thresholded Regression Dinah Shender∗

Source URL: jmlr.org

• Multivariate statistics
• Sparse PCA
• Principal component analysis

Mach Learn:3–39 DOIs10994A majorization-minimization approach to the sparse generalized eigenvalue problem Bharath K. Sriperumbudur · David A. Torres ·

Source URL: eceweb.ucsd.edu

• Multivariate statistics
• Operations research
• Mathematical optimization
• Numerical linear algebra
• Dimension reduction
• Sparse PCA
• Sparse approximation
• Principal component analysis
• Limit of a function
• Convex optimization
• Semidefinite programming
• K-SVD

Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies

Source URL: www.cs.tau.ac.il

• Epigenetics
• DNA methylation
• Differentially methylated regions
• Principal component analysis
• Methylation
• Gene expression
• Epigenome

Best* Case Approximability of Sparse PCA refusing to graduate :-) Aviad Rubinstein (UC Berkeley)

Source URL: www.boazbarak.org

Package ‘irlba’ October 11, 2015 Type Package Title Fast Truncated SVD, PCA and Symmetric Eigendecomposition for Large Dense and Sparse Matrices Version 2.0.0

Source URL: cran.r-project.org

Rate-optimal posterior contraction for sparse PCA

Source URL: www.stat.yale.edu

Composite Loss Functions and Multivariate Regression; Sparse PCA G. Obozinski, B. Taskar, and M. I. JordanJoint covariate selection and joint subspace selection for multiple classification problems. Statistics a

Source URL: mlg.eng.cam.ac.uk

SUPPLEMENT TO “RATE-OPTIMAL POSTERIOR CONTRACTION FOR SPARSE PCA” By Chao Gao and Harrison H. Zhou Yale University In this text, we present proofs of Proposition 2.1, Lemma 5.1, Lemma 5.8, Lemma 5.10, Theorem 4.2, Th

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Sparse Principal Component Analysis Hui Zou∗, Trevor Hastie†, Robert Tibshirani‡ April 26, 2004 Abstract Principal component analysis (PCA) is widely used in data processing and dimensionality

Source URL: www2.imm.dtu.dk