In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …

Semantic image inpainting is a challenging task where large missing regions have to be
filled based on the available visual data. Existing methods which extract information from …

As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

This paper studies the Tensor Robust Principal Component (TRPCA) problem which
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …

Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …

The nuclear norm is widely used as a convex surrogate of the rank function in compressive
sensing for low rank matrix recovery with its applications in image recovery and signal …

Sparsity-constrained optimization problems are common in machine learning, such as
sparse coding, low-rank minimization and compressive sensing. However, most of previous …

Sparsity and missing data problems are very common in spatiotemporal traffic data collected
from various sensing systems. Making accurate imputation is critical to many applications in …