Least squares regression (LSR) is an important machine learning method for feature
extraction, feature selection, and image classification. For the training samples, there are …

Joint image denoising algorithms use the structures of the guidance image as a prior to
restore the noisy target image. While the provided guidance images are helpful to improve …

Human action recognition (HAR) is one of most important tasks in video analysis. Since
video clips distributed on networks are usually untrimmed, it is required to accurately …

Hyperspectral image (HSI) completion is a fundamental problem in image processing and
remote sensing. Typical methods, either perform suboptimally due to lack of appropriate …

Subspace clustering algorithms have been found successful in various applications that
involve two-dimensional data, ie, each example of the data is a matrix. However, most of the …

Abstract Total Variation and Low-Rank regularizations have shown significant successes in
machine learning, data mining, and image processing in past decades. This paper develops …

In recent years, a large number of studies have shown that low rank matrix learning (LRML)
has become a popular approach in machine learning and computer vision with many …

Abstract Recently, tensor completion (TC), namely high-order extension of matrix
completion, has aroused widespread attention. Many priors have been investigated turning …

The low level vision processing methods based on nuclear norm and distance measurement
can reveal the low-rank structure of data matrix and the similarity of data samples, which is …

Classifiers based on least squares regression (LSR) are effective in multi-classification
tasks. However, there are two main problems that greatly limit its performance. First of all …