[HTML][HTML] An overview of clustering methods with guidelines for application in mental health research
Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …
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 …
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
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 …
attention. Recently various effective algorithms have been developed to tackle the task …
Online nonnegative matrix factorization with outliers
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 …
factorization in the presence of outliers. Our framework is particularly suited to large-scale …
Robust graph regularized nonnegative matrix factorization for clustering
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 …
learning problems. In particular, for a dataset without any negative entries, nonnegative …
Online nonnegative matrix factorization with general divergences
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 …
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
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 …
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 …
extensive attention. However, since some data has noise or outliers, these noise or outliers …
Bayesian nonnegative matrix factorization with Dirichlet process mixtures
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 …
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 …
However, relatively fixed graph regularization terms and loss functions have been adopted …