|
Showing 1 - 2 of
2 matches in All Departments
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.
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.
|
You may like...
Merry Christmas
Mariah Carey, Walter Afanasieff, …
CD
R122
R112
Discovery Miles 1 120
|