The notion of regularization has been widely used as a tool to address a number of problems that are usually encountered in Machine Learning. Improving the performance of an estimator by shrinking the norm of the MVU estimator, guarding against overfitting, coping with ill-conditioning, provid
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Sparse data models, where data is assumed to be well represented as a linear combination of a few el...
Classical dictionary learning methods simply normalize dictionary columns at each iteration, and the...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
The sparsity of signals in a certain transform domain or dictionary has been extended in different a...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
12 pages. arXiv admin note: text overlap with arXiv:1104.1436During the past years there has been an...
The rapid development of modern information technology has significantly facilitated the generation,...
Sparsity plays a key role in machine learning for several reasons including interpretability. Interp...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
National Natural Science Foundation of China [61370114]; Beijing Natural Science Foundation [41720...
Over the last decade, learning theory performed significant progress in the development of sophistic...
In system identification, the Akaike Information Criterion (AIC) is a well known method to balance t...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Sparse data models, where data is assumed to be well represented as a linear combination of a few el...
Classical dictionary learning methods simply normalize dictionary columns at each iteration, and the...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
The sparsity of signals in a certain transform domain or dictionary has been extended in different a...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
12 pages. arXiv admin note: text overlap with arXiv:1104.1436During the past years there has been an...
The rapid development of modern information technology has significantly facilitated the generation,...
Sparsity plays a key role in machine learning for several reasons including interpretability. Interp...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
National Natural Science Foundation of China [61370114]; Beijing Natural Science Foundation [41720...
Over the last decade, learning theory performed significant progress in the development of sophistic...
In system identification, the Akaike Information Criterion (AIC) is a well known method to balance t...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Sparse data models, where data is assumed to be well represented as a linear combination of a few el...
Classical dictionary learning methods simply normalize dictionary columns at each iteration, and the...
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