Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum
computational framework for solving quantum or classical optimization tasks. Here, we …

Phasor measurement unit (PMU) data quality problems, such as data loss and modification
caused by communication contingencies and cyber attacks, threaten the reliability and …

Deep matrix factorization can be regarded as an extension of traditional matrix factorization
to help improve applications like social image tag refinement, image retrieval, and face …

Nonnegative matrix factorization (NMF) is one of the best-known multivariate data analysis
techniques. The NMF uniqueness and its rank selection are two major open problems in this …

This article is concerned with a problem of relational factorization which engages fuzzy
relational calculus. It forms an interesting alternative to the method of nonnegative matrix …

The development of cyber‐physical power systems raises concerns about the data quality
issue of phasor measurement units (PMUs). Low signal‐to‐noise ratios (SNRs) and data …

Deep matrix factorization has been demonstrated in extracting hierarchical knowledge
describing micro-video characteristics. However, the complementary information across …

We introduce a probabilistic model with implicit norm regularization for learning nonnegative
matrix factorization (NMF) that is commonly used for predicting missing values and finding …