To deal with the curse of dimensionality, three regularization schemes are used: a reduced-rank model, penalization of the covariance matrix, and regularization of the basis-expanded predictor set. The latter allows us to flexibly model associations while controlling for overfitting. The modeling ...
Joint Sparse and Low-Rank Multitask Learning with Laplacian-Like Regularization for Hyperspectral Classification Multitask learning (MTL) has recently provided significant performance improvements in supervised classification of hyperspectral images (HSIs) by incorporating shared information across multiple tasks...
Our analysis brings into sharp focus the soft-thresholding form of the optimal weights, the non-convex nature of the associated shrinkage function and explains why matrix regularization via singular value thresholding with convex penalty functions (such as the nuclear norm) will always be suboptimal....
HIGH - QUALITY IMAGE RESTORATION USING LOW - RANK PATCH REGULARIZATION AND GLOBAL STRUCTURE SPARSITYS. Izas AhamedR. PoornimaIJARIIE
High-quality Image Restoration Using Low-Rank Patch Regularization and Global Structure Sparsitydoi:10.1109/tip.2018.2874284Mingli ZhangChristian DesrosiersIEEE
l 1 -regularizationProximal alternating minimizationFor the best sparse rank-1 approximation to higher-order tensors, we propose a proximal alternating minimization method in which each iteration can be easily computed. Its global convergence is established without any assumption and the numerical ...
Extensive numerical studies are carried out using both simulated and real datasets to show the proposed approach鈥檚 robust performance compared to the existing regularization approaches. Supplementary materials for this article are available online.
A sparse rank-1 approximation algorithm for high-order tensorsSparse rank-1 approximationl(1)-regularizationProximal alternating minimizationFor the best sparse rank-1 approximation to higher-order tensors, we propose a proximal alternating minimization method in which each iteration can be easily ...
We develop an efficient estimation procedure based on the trace norm regularization, which explicitly imposes the low rank structure of coefficient matrices. When both the dimension of response matrix and that of covariate vector diverge at the exponential order of the sample size, we investigate the...
where 𝜆λ is a trade-off parameter and ∥·∥𝑙∥·∥l also indicates a specific regularization technique for the error term. After introducing an extra dictionary matrix, the more general rank minimization problem can be applied to more real mixed data as follows: min𝑪,𝑷𝑟𝑎𝑛...