Sparse representation of Brillouin spectrum using dictionary learning
نویسندگان
چکیده
منابع مشابه
Accelerated Dictionary Learning for Sparse Signal Representation
Learning sparsifying dictionaries from a set of training signals has been shown to have much better performance than pre-designed dictionaries in many signal processing tasks, including image enhancement. To this aim, numerous practical dictionary learning (DL) algorithms have been proposed over the last decade. This paper introduces an accelerated DL algorithm based on iterative proximal metho...
متن کاملDictionary Learning Algorithms for Sparse Representation
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally...
متن کاملEvaluating Dictionary Learning for Sparse Representation Algorithms using SMALLbox
SMALLbox is an open source MATLAB toolbox aiming at becoming a testing ground for the exploration of new provably good methods to obtain inherently data-driven sparse models, which are able to cope with large-scale and complicated data. I. SMALLBOX EVALUATION FRAMEWORK The field of sparse representations has gained a huge interest in recent years, in particular in applications such as compresse...
متن کاملIncrementally Built Dictionary Learning for Sparse Representation
Extracting sparse representations with Dictionary Learning (DL) methods has led to interesting image and speech recognition results. DL has recently been extended to supervised learning (SDL) by using the dictionary for feature extraction and classification. One challenge with SDL is imposing diversity for extracting more discriminative features. To this end, we propose Incrementally Built Dict...
متن کاملShape Prior Modeling Using Sparse Representation and Online Dictionary Learning
The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Optics Express
سال: 2020
ISSN: 1094-4087
DOI: 10.1364/oe.391970