Results 21 to 30 of about 1,917,955 (191)

Robust Community Detection in Graphs

IEEE Access, 2021
Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint ...
Esraa M. Al-Sharoa, Bara' M. Ababneh, Mahmood A. Alkhassaweneh   +2 more
doaj   +1 more source

Community Detection Algorithm Based on Nonnegative Matrix Factorization and Improved Density Peak Clustering

IEEE Access, 2020
Community detection is a critical issue in the field of complex networks. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks.
Hong Lu, Xiaoshuang Sang, Qinghua Zhao, Jianfeng Lu   +3 more
doaj   +1 more source

CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization [PDF]

Neurocomputing, 2018
Abstract Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis.
Li-Ping Tian 0001, Ping Luo 0003, Haiying Wang 0001, Huiru Zheng, Fang-Xiang Wu   +4 more
openaire   +2 more sources

Symmetric Nonnegative Matrix Trifactorization [PDF]

, 2022
The Symmetric Nonnegative Matrix Trifactorization (SN-Trifactorization) is a factorization of an $n \times n$ nonnegative symmetric matrix $A$ of the form $BCB^T$, where $C$ is a $k \times k$ symmetric matrix, and both $B$ and $C$ are required to be ...
Bukovšek, Damjana Kokol, Šmigoc, Helena, Helena Šmigoc, Damjana Kokol Bukovšek, Kokol-Bukovšek, Damjana   +4 more
core   +1 more source

Directional clustering through matrix factorization [PDF]

, 2016
This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
core   +1 more source

Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]

, 2009
Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Braak, C.J.F., ter, Kourmpetis, Y.I.A., Kiers, H.A.L., Bink, M.C.A.M.   +3 more
core   +1 more source

Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020
We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy.
Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park   +5 more
openaire   +3 more sources

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology [PDF]

, 2012
Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data.
Clare M. Lee, Vass J. Keith, D R Haggart, Lee Clare M., Mudaliar, MAV, Higham, Desmond, Vass, D.J., Manikhandan A V Mudaliar, Gino Miele, Mudaliar, Manikhandan A.V., Haggart, D. R., Mudaliar, M.A.V., Higham, Desmond J., Haggart D. R., Crowther, Daniel, D. R. Haggart, Lee, C.M., Lee, Clare M., Mudaliar Manikhandan A. V., J Keith Vass, Higham Desmond J., Wolf, C.R., Higham, D.J., Wolf C. Roland, Miele Gino, Mudaliar, Manikhandan A. V., Miele, Geno, Desmond J Higham, Clare M Lee, C. Roland Wolf, Miele, G., Manikhandan A. V. Mudaliar, C Roland Wolf, Crowther Daniel, Vass, J.K., Miele, Gino, J. Keith Vass, Crowther, D., Crowther, Daniel; id_orcid, Daniel Crowther, Vass, J. Keith, Desmond J. Higham, Haggart, D.R., Wolf, C. Roland, Lee, Clare   +44 more
core   +1 more source

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxiciology [PDF]

, 2012
Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data.
Mudaliar, Manikhandan A. V., Miele, Geno, Higham, Desmond J., Crowther, Daniel, Lee, Clare M., Vass, J. Keith, Haggart, D.R., Wolf, C. Roland   +7 more
core   +2 more sources

Inexact Block Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization [PDF]

IEEE Transactions on Signal Processing, 2017
Symmetric nonnegative matrix factorization (SNMF) is equivalent to computing a symmetric nonnegative low rank approximation of a data similarity matrix. It inherits the good data interpretability of the well-known nonnegative matrix factorization technique and have better ability of clustering nonlinearly separable data.
Qingjiang Shi, Haoran Sun, Songtao Lu, Mingyi Hong 0001, Meisam Razaviyayn   +4 more
openaire   +2 more sources