Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation ...

Sparse Gaussian graphical models characterize sparse dependence relationships between random variables in a network. To estimate multiple related Gaussian graphical models on the same set of variables, we formulate a hierarchical model, which leads to an optimization problem with a nonconvex log-shift penalty function. We show that under mild conditions the optimization problem is convex despit...

Sparse coding has achieved great success in various image restoration tasks. However, if the sparse representation coefficients of structure (low-frequency information) and texture (high-frequency components are under same penalty constraint, effect may not be ideal. In this paper, an denoising model combining mixed norm weighted nuclear as regularization terms is proposed. The proposed simulta...

Recently, sparse coding has been widely used in many applications ranging from image recovery to pattern recognition. The low mutual coherence of a dictionary is an important property that ensures the optimality of the sparse code generated from this dictionary. Indeed, most existing dictionary learning methods for sparse coding either implicitly or explicitly tried to learn an incoherent dicti...

We propose a robust edge extraction algorithm based on Principal component analysis (PCA) for Poisson noise reduction. In the proposed edge detection, the image is firstly transformed to PCA subspace with sparse PCA basis functions and then the noisy components are removed by a soft threshold (Shrinkage). The proposed edge extraction method, which is used as a preprocessing step of the reconstr...

In this paper, we present a new speech enhancement method based on robust principal component analysis. In the proposed method, noisy signal is transformed into time-frequency domain where background noise is assumed as a low-rank component and human speech is regarded as a sparse compone. An inexact augmented Lagrange multipliers algorithm is conducted for solving the noise and speech separati...

We present a method to combine error-correction coding and spectral-efficient modulation for transmission over the Additive White Gaussian Noise (AWGN) channel. The code employs signal shaping which can provide a so-called shaping gain. The code belongs to the family of sparse graph codes for which efficient decoding algorithms can be derived. Simulation results show that the performance of the...

In this paper, we analyze a sparse nonlinear inverse scattering problem arising in microwave imaging and numerically solved it for retrieving dielectric contrast from measured fields. In sparsity reconstruction, contrast profiles are a priori assumed to be sparse with respect to a certain base. We proposed an approach which is motivated by a Tikhonov functional incorporating a sparsity promotin...

A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue, we propose Bayesian masking (BM), a sparse estimation method which imposes ...