Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for
sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The …

This paper investigates online composite optimization in dynamic environments, where each
objective or loss function contains a time-varying nondifferentiable regularizer. To resolve it …

Stochastic approximation techniques have been used in various contexts in data science.
We propose a stochastic version of the forward-backward algorithm for minimizing the sum …

Applications involving large-scale dictionary learning tasks motivate well online optimization
algorithms for generally non-convex and non-smooth problems. In this big data context, the …

We propose a novel sparsity-aware adaptive filtering algorithm based on iterative use of
weighted soft-thresholding. The weights are determined based on a rough local …

This paper investigates online composite optimization in dynamic environments, where each
objective or loss function contains a time-varying nondifferentiable regularizer. To resolve it …

We propose a proximal splitting approach to regularized distributed estimation over
networks employing diffusion adaptation strategies. Playing a central role in the proposed …

Observing that sparse systems are almost smooth, we propose to utilize the newly-
introduced adaptively-weighted total variation (AWTV) for sparse system identification. In our …

In this paper, we propose a robust sparsity-aware adaptive filtering algorithm under
impulsive noise environment, by using the Huber loss function in the frame of adaptive …