Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

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 …

Reliable data measurement is considered to be one of the critical ingredients for variant
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …

To improve the accuracy of color image completion with missing entries, we present a
recovery method based on generalized higher-order scalars. We extend the traditional …

Recent efforts to unravel the mystery of implicit regularization in deep learning have led to a
theoretical focus on matrix factorization—matrix completion via linear neural network. As a …

Recently, online reviews are used increasingly by individuals and organizations for making
purchase and business decisions. Unfortunately, driven by profit and fame, spammers post …

Tensor completion using multiway delay-embedding transform (MDT)(or Hankelization)
suffers from the large memory requirement and high computational cost in spite of its high …

In this article, we propose a novel tensor learning and coding model for third-order data
completion. The aim of our model is to learn a data-adaptive dictionary from given …

The low-tubal-rank tensor model has been recently proposed for real-world
multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem …

Data scientists seeking a good supervised learning model on a dataset have many choices
to make: they must preprocess the data, select features, possibly reduce the dimension …