Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …

Most existing subspace clustering methods hinge on self-expression of handcrafted
representations and are unaware of potential clustering errors. Thus they perform …

Recently, a prevailing trend of user generated content (UGC) on social media sites is the
emerging micro-videos. Microvideos afford many potential opportunities ranging from …

Multi-way or tensor data analysis has attracted increasing attention recently, with many
important applications in practice. This article develops a tensor low-rank representation …

Feature extraction plays a significant role in pattern recognition. Recently, many
representation-based feature extraction methods have been proposed and achieved …

As a popular feature extraction algorithm, the 2D local preserving projections (2DLPP)
algorithm has been successfully applied in many fields. Using 2D image representation, the …

Low-rank tensor analysis is important for various real applications in computer vision.
However, existing methods focus on recovering a low-rank tensor contaminated by …

Transfer learning is generally exploited to adapt well-established source knowledge for
learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see …

This paper proposes a novel method, called robust latent subspace learning (RLSL), for
image classification. We formulate an RLSL problem as a joint optimization problem over …