Variance-Component Based Sparse Signal Reconstruction and Model Selection
نویسندگان
چکیده
منابع مشابه
Sparse analysis model based dictionary learning and signal reconstruction
Sparse representation has been studied extensively in the past decade in a variety of applications, such as denoising, source separation and classification. Earlier effort has been focused on the well-known synthesis model, where a signal is decomposed as a linear combination of a few atoms of a dictionary. However, the analysis model, a counterpart of the synthesis model, has not received much...
متن کاملSparse and Robust Signal Reconstruction
Many problems in signal processing and statistical inference are based on finding a sparse solution to an undetermined linear system. The reference approach to this problem of finding sparse signal representations, on overcomplete dictionaries, leads to convex unconstrained optimization problems, with a quadratic term l2, for the adjustment to the observed signal, and a coefficient vector l1-no...
متن کاملSparse Signal Reconstruction: LASSO and Cardinality Approaches
The paper considers several optimization problem statements for signal sparse reconstruction problems. We tested performance of AORDA Portfolio Safeguard (PSG) package with different problem formulations. We solved several medium-size test problems with cardinality functions: (a) minimize L1-error of regression subject to a constraint on cardinality of the solution vector; (b) minimize cardinal...
متن کاملSparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...
متن کاملInter-atom Interference Mitigation for Sparse Signal Reconstruction Using Semi-blindly Weighted Minimum Variance Distortionless Response
The feasibility of sparse signal reconstruction depends heavily on the inter-atom interference of redundant dictionary. In this paper, a semi-blindly weighted minimum variance distortionless response (SBWMVDR) is proposed to mitigate the inter-atom interference. Examples of direction of arrival estimation are presented to show that the orthogonal match pursuit (OMP) based on SBWMVDR performs be...
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: IEEE Transactions on Signal Processing
سال: 2010
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2010.2044828