[HTML][HTML] An overview of clustering methods with guidelines for application in mental health research

CX Gao, D Dwyer, Y Zhu, CL Smith, L Du, KM Filia… - Psychiatry …, 2023 - Elsevier
Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …

Regularized non-negative matrix factorization for identifying differentially expressed genes and clustering samples: A survey

JX Liu, D Wang, YL Gao, CH Zheng… - … /ACM transactions on …, 2017 - ieeexplore.ieee.org
Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction,
has been applied in many fields. It is based on the idea that negative numbers are physically …

Robust bi-stochastic graph regularized matrix factorization for data clustering

Q Wang, X He, X Jiang, X Li - IEEE transactions on pattern …, 2020 - ieeexplore.ieee.org
Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …

Online nonnegative matrix factorization with outliers

R Zhao, VYF Tan - IEEE Transactions on Signal Processing, 2016 - ieeexplore.ieee.org
We propose a unified and systematic framework for performing online nonnegative matrix
factorization in the presence of outliers. Our framework is particularly suited to large-scale …

Robust graph regularized nonnegative matrix factorization for clustering

C Peng, Z Kang, Y Hu, J Cheng, Q Cheng - ACM Transactions on …, 2017 - dl.acm.org
Matrix factorization is often used for data representation in many data mining and machine-
learning problems. In particular, for a dataset without any negative entries, nonnegative …

Online nonnegative matrix factorization with general divergences

R Zhao, V Tan, H Xu - Artificial Intelligence and Statistics, 2017 - proceedings.mlr.press
We develop a unified and systematic framework for performing online nonnegative matrix
factorization under a wide variety of important divergences. The online nature of our …

Radar: Road obstacle identification for disaster response leveraging cross-domain urban data

L Chen, X Fan, L Wang, D Zhang, Z Yu, J Li… - Proceedings of the …, 2018 - dl.acm.org
Typhoons and hurricanes cause extensive damage to coast cities annually, demanding
urban authorities to take effective actions in disaster response to reduce losses. One of the …

Robust clustering with sparse corruption via ℓ2, 1, ℓ1 norm constraint and Laplacian regularization

M Zhao, J Liu - Expert Systems with Applications, 2021 - Elsevier
Clustering has been applied in machine learning, data mining and so on, and has received
extensive attention. However, since some data has noise or outliers, these noise or outliers …

Bayesian nonnegative matrix factorization with Dirichlet process mixtures

C Li, HB Xie, K Mengersen, X Fan… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source
separation, signal processing and machine learning. A number of algorithms that can infer …

Auto-adjustable hypergraph regularized non-negative matrix factorization for image clustering

H Zuo, S Li, C Liang, J Li - Pattern Recognition, 2024 - Elsevier
Non-negative matrix factorization (NMF) is an effective method for image clustering.
However, relatively fixed graph regularization terms and loss functions have been adopted …