The why and how of nonnegative matrix factorization
N Gillis - … , optimization, kernels, and support vector machines, 2014 - books.google.com
Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …
high-dimensional data as it automatically extracts sparse and meaningful features from a set …
[图书][B] Nonnegative matrix factorization
N Gillis - 2020 - SIAM
Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …
Computing large-scale matrix and tensor decomposition with structured factors: A unified nonconvex optimization perspective
During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have
become indispensable tools for signal processing, machine learning, and data science …
become indispensable tools for signal processing, machine learning, and data science …
Robust tensor ring-based graph completion for incomplete multi-view clustering
Incomplete multi-view clustering (IMVC) aims to enhance clustering performance by
leveraging complementary information from multi-view data, even in the presence of missing …
leveraging complementary information from multi-view data, even in the presence of missing …
Bounded simplex-structured matrix factorization: Algorithms, identifiability and applications
In this article, we propose a new low-rank matrix factorization model dubbed bounded
simplex-structured matrix factorization (BSSMF). Given an input matrix and a factorization …
simplex-structured matrix factorization (BSSMF). Given an input matrix and a factorization …
Accelerating regularized tensor decomposition using the alternating direction method of multipliers with multiple Nesterov's extrapolations
Tensor decomposition is an essential tool for multiway signal processing. At present, large-
scale high-order tensor data require fast and efficient decomposing algorithms. In this paper …
scale high-order tensor data require fast and efficient decomposing algorithms. In this paper …
Block differential evolution
R Khosrowshahli… - 2023 IEEE Congress on …, 2023 - ieeexplore.ieee.org
In order to solve huge-scale optimization problems, many evolutionary algorithms have
been proposed. In this paper, we introduce Block Differential Evolution (BDE) algorithm. The …
been proposed. In this paper, we introduce Block Differential Evolution (BDE) algorithm. The …
Matrix Completion Based on Low-Rank and Local Features Applied to Images Recovery and Recommendation Systems
Y Zhang, K Xu, S Liang, C Zhao - IEEE Access, 2022 - ieeexplore.ieee.org
Recent advances have shown that the challenging problem of matrix completion arises from
real-world applications, such as image recovery, and recommendation systems. Existing …
real-world applications, such as image recovery, and recommendation systems. Existing …
Tangent space based alternating projections for nonnegative low rank matrix approximation
In this article, we develop a new alternating projection method to compute nonnegative low
rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix …
rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix …
Doubly Accelerated Proximal Gradient for Nonnegative Tensor Decomposition
D Wang - International Symposium on Neural Networks, 2024 - Springer
The accelerated proximal gradient (APG) is a classical algorithm for nonnegative tensor
decomposition. The APG employs variable extrapolation to accelerate the computation …
decomposition. The APG employs variable extrapolation to accelerate the computation …