We propose a new Group Feature Selection method for Discriminative Correlation Filters
(GFS-DCF) based visual object tracking. The key innovation of the proposed method is to …

The advancement of single-cell sequencing technology in recent years has provided an
opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of …

Deep convolutional neural networks (CNNs) have demonstrated its extraordinary power on
various visual tasks like object detection and classification. However, it is still challenging to …

Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big
challenge in the design and development of brain-computer interfaces (BCIs). In view of the …

Hyperspectral unmixing has attracted much attention in recent years. Single sparse
unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number …

Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradients
with momentum where a controlled and properly scaled Gaussian noise is added to the …

For different parameterizations (mappings from parameters to predictors), we study the
regularization cost in predictor space induced by $ l_2 $ regularization on the parameters …

This paper presents a framework for smooth optimization of objectives with ℓq and ℓp, q
regularization for (structured) sparsity. Finding solutions to these non-smooth and possibly …

In this work, we propose a modified inexact Levenberg–Marquardt method with the descent
property for solving nonlinear equations. A novel feature of the proposed method is that one …

This paper considers the image recovery problem by taking group sparsity into account as
the prior knowledge. This problem is formulated as a group sparse optimization over the …