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Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues
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DOI：10.1146/annurev-statistics-022513-115643.png)
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Abstract
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Regularization is a widely used technique throughout statistics, machine learning, and applied mathematics. Modern applications in science and engineering lead to massive and complex data sets, which motivate the use of more structured types of regularizers. This survey provides an overview of the use of structured regularization in high-dimensional statistics, including regularizers for group-structured and hierarchical sparsity, low-rank matrices, additive and multiplicative matrix decomposition, and high-dimensional nonparametric models. It includes various examples with motivating applications; it also covers key aspects of statistical theory and provides some discussion of efficient algorithms.
Keywords:
regularization
M-estimation
high-dimensional statistics
statistical machine learning
algorithms
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