Sparse Modeling: Theory, Algorithms, and Applications
Sparse Modeling: Theory, Algorithms, and Applications
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DOI：
10.1201/b17758
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发表时间：
2014-12
期刊：
影响因子：
--
通讯作者：
I. Rish;G. Grabarnik
中科院分区：
文献类型：
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作者：
I. Rish;G. Grabarnik
稀疏模型在科学应用中特别有用，例如遗传或神经影像数据中的生物标志物发现，其中预测模型的可解释性至关重要。稀疏性还可以显着提高信号处理的成本效率。稀疏建模：理论、算法和应用介绍了不断发展的稀疏建模领域，包括应用示例、产生稀疏解决方案的问题公式、寻找此类解决方案的算法以及稀疏恢复的最新理论结果。这本书让您了解稀疏性相关的最新进展，并将激励您继续学习该领域。作者首先提出了一些激励性的例子，并对稀疏建模的最新关键发展进行了高层次的调查。然后，本书描述了涉及常用稀疏性执行工具的优化问题，提出了重要的理论结果，并讨论了几种用于寻找稀疏解决方案的最先进算法。作者继续解决各种稀疏恢复问题，将基本公式扩展到更复杂的结构化稀疏形式和不同的损失函数。他们还研究了一类特定的稀疏图形模型，并涵盖字典学习和稀疏矩阵分解。
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.