The emergence of Big Data raises the question how to model economic relations when there is a large number possible explanatory variables. We revisit issue by comparing possibility using dense or sparse models in Bayesian approach, allowing for variable selection and shrinkage. More specifically, we discuss results reached Giannone, Lenza Primiceri (2020) through “Spike-and-Slab” prior, which s...

In this paper, we proposed a novel sparse coding algorithm by using the class labels to constrain the learning of codebook and sparse code. We not only use the class label to train the classifier, but also use it to construct class conditional codewords to make the sparse code as discriminative as possible. We first construct ideal sparse codes with regarding to the class conditional codewords,...

In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly suitable for modeling unimodal super-Gaussian data. We demonstrate that the NIG density provides a very good fit to the sparsely coded data, obtained here via an independent component analysis (ICA)...

Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images the recent decade. regularization ℓ1 can reduce noise level suppress sidelobes. However, suppression sidelobes by sparse often pays price losing information weak targets. Therefore, method combining spatially variant apodization is proposed this paper, which noise, retain detail i...

Properties of a novel algorithm called non-negative matrix factorization (NMF), are studied. NMF can discover substructures and can provide estimations about the presence or the absence of those, being attractive for completion of missing information. We have studied the working and learning capabilities of NMF networks. Performance was improved by adding sparse code shrinkage (SCS) algorithm t...

Modeling a correlation matrix R can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocor...

This paper considers solving the unconstrained lq-norm (0 ≤ q < 1) regularized least squares (lq-LS) problem for recovering sparse signals in compressive sensing. We propose two highly efficient first-order algorithms via incorporating the proximity operator for nonconvex lq-norm functions into the fast iterative shrinkage/thresholding (FISTA) and the alternative direction method of multipliers...

Watermarking with oblivious detection and high robustness capabilities together is still a challenging problem up to now. The existing methods are either robust or oblivious but it is diicult to achieve both goals simultaneously. In this paper, oblivious detection is formulated as a blind source separation problem by regarding the hidden watermarks as noises. To keep high robustness, our non-ob...

Single channel speech enhancement is an important problem in practice. One of the well used single channel speech enhancement method, spectral subtraction, can only work for stationary noise. Another method based on Kalman filtering is able to work with non-stationary signals. However, it can only produce optimal estimation of speech signal which is corrupted by Gaussian noise. In practice, spe...

A new locally sparse (i.e., zero on some subregions) estimator for coefficient functions in functional linear regression models is developed based on a novel functional regularization technique called “fSCAD”. The nice shrinkage property of fSCAD allows the proposed estimator to locate null subregions of coefficient functions without over shrinking non-zero values of coefficient functions. Addi...