Joint sparse representation (JSR) has been a popular technique for hyperspectral image
classification, where a testing pixel and its spatial neighbors are simultaneously …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

In machine learning research, the proximal gradient methods are popular for solving various
optimization problems with non-smooth regularization. Inexact proximal gradient methods …

Multi-view hashing has gained considerable research attention in efficient multimedia
studies due to its promising performance on heterogeneous data from various sources …

Linear regression has shown an effective tool for face recognition in recent years. Most
existing linear regression based methods are devised for grayscale image based face …

In this paper, we study the performance of robust learning with Huber loss. As an alternative
to traditional empirical risk minimization schemes, Huber regression has been extensively …

In a regression setup, we study in this brief the performance of Gaussian empirical gain
maximization (EGM), which includes a broad variety of well-established robust estimation …

Sparse representation of kernel based regression (KBR) has received considerable
attention in recent years. Studies on sparse KBR can be divided into two distinct groups …

Ordinal regression (OR) methods are designed for a type of classification problems where
data labels have natural orders. In practice, data may be corrupted by label noise, which …

Sparse additive machines (SAMs) have shown competitive performance on variable
selection and classification in high-dimensional data due to their representation flexibility …