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 …
Continuous Semi-Supervised Nonnegative Matrix Factorization
MR Lindstrom, X Ding, F Liu, A Somayajula, D Needell - Algorithms, 2023 - mdpi.com
Nonnegative matrix factorization can be used to automatically detect topics within a corpus
in an unsupervised fashion. The technique amounts to an approximation of a nonnegative …
in an unsupervised fashion. The technique amounts to an approximation of a nonnegative …
Distributed out-of-memory NMF on CPU/GPU architectures
We propose an efficient distributed out-of-memory implementation of the non-negative
matrix factorization (NMF) algorithm for heterogeneous high-performance-computing …
matrix factorization (NMF) algorithm for heterogeneous high-performance-computing …
Accelerated Constrained Sparse Tensor Factorization on Massively Parallel Architectures
This study presents the first constrained sparse tensor factorization (cSTF) framework that
optimizes and fully offloads computation to massively parallel GPU architectures, and the …
optimizes and fully offloads computation to massively parallel GPU architectures, and the …
Cauchy balanced nonnegative matrix factorization
Abstract Nonnegative Matrix Factorization (NMF) plays an important role in many data
mining and machine learning tasks. Standard NMF uses the Frobenius norm as the loss …
mining and machine learning tasks. Standard NMF uses the Frobenius norm as the loss …
Acc-SpMM: Accelerating General-purpose Sparse Matrix-Matrix Multiplication with GPU Tensor Cores
H Zhao, S Li, J Wang, C Zhou, J Wang, Z Xin… - arXiv preprint arXiv …, 2025 - arxiv.org
General-purpose Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental kernel in
scientific computing and deep learning. The emergence of new matrix computation units …
scientific computing and deep learning. The emergence of new matrix computation units …
A proximal alternating-direction-method-of-multipliers-based nonnegative latent factor model
F Bi, D Wu - 2021 IEEE International Conference on Big …, 2021 - ieeexplore.ieee.org
A high dimensional and sparse (HiDS) matrix is commonly found in big data-related
industrial applications. An alternating direction method of multipliers (ADMM)-based …
industrial applications. An alternating direction method of multipliers (ADMM)-based …
Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress
A number of methods have been developed for unsupervised network representation
learning--ranging from classical methods based on the graph spectra to recent random walk …
learning--ranging from classical methods based on the graph spectra to recent random walk …
Parallel hierarchical clustering using rank-two nonnegative matrix factorization
Nonnegative Matrix Factorization (NMF) is an effective tool for clustering nonnegative data,
either for computing a flat partitioning of a dataset or for determining a hierarchy of similarity …
either for computing a flat partitioning of a dataset or for determining a hierarchy of similarity …
Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State relationships
Single cell RNA sequencing (scRNA-seq) is a gene expression profiling technique that is
presently revolutionizing the study of complex cellular systems in the biological sciences …
presently revolutionizing the study of complex cellular systems in the biological sciences …