A survey of community detection in complex networks using nonnegative matrix factorization
Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …
Nonnegative matrix factorizations for clustering: A survey
This chapter introduces the basic formulations of nonnegative matrix factorization (NMF) and
outlines the theoretical foundations on NMF for clustering and presents the equivalence …
outlines the theoretical foundations on NMF for clustering and presents the equivalence …
Robust structured nonnegative matrix factorization for image representation
Dimensionality reduction has attracted increasing attention, because high-dimensional data
have arisen naturally in numerous domains in recent years. As one popular dimensionality …
have arisen naturally in numerous domains in recent years. As one popular dimensionality …
Algorithms of unconstrained non-negative latent factor analysis for recommender systems
Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …
Latent factor-based recommenders relying on extended stochastic gradient descent algorithms
High-dimensional and sparse (HiDS) matrices generated by recommender systems contain
rich knowledge regarding various desired patterns like users' potential preferences and …
rich knowledge regarding various desired patterns like users' potential preferences and …
Robust perceptual image hashing based on ring partition and NMF
This paper designs an efficient image hashing with a ring partition and a nonnegative matrix
factorization (NMF), which has both the rotation robustness and good discriminative …
factorization (NMF), which has both the rotation robustness and good discriminative …
Symmetric nonnegative matrix factorization: Algorithms and applications to probabilistic clustering
Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in
various applications including image processing and semantic analysis of documents. This …
various applications including image processing and semantic analysis of documents. This …
PPHOPCM: Privacy-preserving high-order possibilistic c-means algorithm for big data clustering with cloud computing
As one important technique of fuzzy clustering in data mining and pattern recognition, the
possibilistic c-means algorithm (PCM) has been widely used in image analysis and …
possibilistic c-means algorithm (PCM) has been widely used in image analysis and …
Crime topic modeling
D Kuang, PJ Brantingham, AL Bertozzi - Crime Science, 2017 - Springer
The classification of crime into discrete categories entails a massive loss of information.
Crimes emerge out of a complex mix of behaviors and situations, yet most of these details …
Crimes emerge out of a complex mix of behaviors and situations, yet most of these details …
Multi-view clustering guided by unconstrained non-negative matrix factorization
Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known
method for handling high-dimensional multi-view data. To satisfy the non-negativity …
method for handling high-dimensional multi-view data. To satisfy the non-negativity …