A survey on deep matrix factorizations
Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …
linear dimensionality reduction techniques able to extract the information contained in large …
The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
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 …
methods result in misleading results and waste of computing resources due to lack of timely …
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 …
Proximal alternating-direction-method-of-multipliers-incorporated nonnegative latent factor analysis
High-dimensional and incomplete (HDI) data subject to the nonnegativity constraints are
commonly encountered in a big data-related application concerning the interactions among …
commonly encountered in a big data-related application concerning the interactions among …
A fast nonnegative autoencoder-based approach to latent feature analysis on high-dimensional and incomplete data
F Bi, T He, X Luo - IEEE Transactions on Services Computing, 2023 - ieeexplore.ieee.org
High-Dimensional and Incomplete (HDI) data are frequently encountered in various Big
Data-related applications. Despite its incompleteness, an HDI data repository contains rich …
Data-related applications. Despite its incompleteness, an HDI data repository contains rich …
A novel underdetermined blind source separation algorithm of frequency-hopping signals via time-frequency analysis
To address the significant performance degradation of conventional underdetermined blind
source separation algorithms for frequency-hopping (FH) signals under time-frequency (TF) …
source separation algorithms for frequency-hopping (FH) signals under time-frequency (TF) …
Blind image separation for the debonding defects recognition of the solid propellant rocket motor cladding layer using pulse thermography
Blind image separation based on wavelet transform (WT) enhancement algorithm for the
pulse thermography was introduced as a novel modality to recognize the debonding defects …
pulse thermography was introduced as a novel modality to recognize the debonding defects …
Low-rank nonnegative tensor approximation via alternating projections and sketching
A Sultonov, S Matveev, S Budzinskiy - Computational and Applied …, 2023 - Springer
We show how to construct nonnegative low-rank approximations of nonnegative tensors in
Tucker and tensor train formats. We use alternating projections between the nonnegative …
Tucker and tensor train formats. We use alternating projections between the nonnegative …
Simplex-structured matrix factorization: Sparsity-based identifiability and provably correct algorithms
M Abdolali, N Gillis - SIAM Journal on Mathematics of Data Science, 2021 - SIAM
In this paper, we provide novel algorithms with identifiability guarantees for simplex-
structured matrix factorization (SSMF), a generalization of nonnegative matrix factorization …
structured matrix factorization (SSMF), a generalization of nonnegative matrix factorization …