KERNEL METHODS FOR INDEPENDENCE MEASUREMENT WITH COEFFICIENT CONSTRAINTS

DOI10.1142/S0219691314500064zbMath1311.62067OpenAlexW2153858539MaRDI QIDQ2874062

Jitao Wu, Heng Chen

Publication date: 28 January 2014

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691314500064

zbMATH Keywords

independenceconstrained covariancecoefficient constraintkernel canonical correlation

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10)

Related Items (2)

Convergence rate of SVM for kernel-based robust regression ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs

Cites Work

• Convergence rate of kernel canonical correlation analysis
• Support vector machines regression with \(l^1\)-regularizer
• Classifiers of support vector machine type with \(\ell_1\) complexity regularization
• Learning Theory
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution

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