Nowadays, big data analysis has become an important approach in social information
network. However, the social information may not be distributed independently and …

Least squares regression (LSR) and its extended methods are widely used for image
classification. However, these LSR-based methods do not consider the importance of global …

Subspace projection has been widely studied to implement the feature extraction for face
recognition. However, it scarcely facilitates exploring the label information in the subspace …

Abstract Discriminative Least Squares Regression (DLSR) is an approach that is more
popularly used in multi-classification tasks, which employs an ε-draggings technique to …

Estimating sequences introduced by Nesterov is an efficient trick to accelerate gradient
descent (GD). The stochastic version of estimating sequences is also successfully used to …

Deep neural networks typically require a large number of accurately labeled images for
training with cross-entropy loss, and often overfit noisy labels. Contrastive learning has …

Many feature extraction methods based on subspace learning have been proposed and
applied with good performance. Most existing methods fail to achieve a balance between …

Although data mining plays an essential role in enhancing business decision-making,
currently, there are hardly any suitable online platforms available that can facilitate …

Linear representation based methods have been extensively applied in image recognition,
especially for those with noise, illumination changes, and occlusions. However, most …

This paper proposes a novel method, called Regularized Denoising Latent Subspace based
Linear Regression (RDLSLR), for noisy image classification. RDLSLR model divides the …