Results 181 to 190 of about 82,948 (222)

Weighted nonnegative matrix factorization

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete.
Yong-Deok Kim, Seungjin Choi
openaire   +1 more source

Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach

IEEE Transactions on Industrial Informatics, 2020
Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns.
Yan Song, Ming Li, Xin Luo, Guisong Yang, Chongjing Wang   +4 more
semanticscholar   +1 more source

Robust semi-supervised nonnegative matrix factorization for image clustering

Pattern Recognition, 2020
Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algorithms are sensitive to noisy data, or fail to fully utilize
Siyuan Peng, W. Ser, Badong Chen, Zhiping Lin   +3 more
semanticscholar   +1 more source

Elastic Nonnegative Matrix Factorization

2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018
Nonnegative Matrix Factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process.
Peter Ballen, Sudipto Guha
openaire   +1 more source

Elastic nonnegative matrix factorization

Pattern Recognition, 2019
Abstract Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust l2,1-norm to measure the quality of factorization, given the assumption of i.i.d Gaussian noise model and i.i.d Laplacian noise model, respectively.
He Xiong, Deguang Kong
openaire   +1 more source

Manifold Peaks Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems
Nonnegative matrix factorization (NMF) has attracted increasing interest for its high interpretability in recent years. It is shown that the NMF is closely related to fuzzy k -means clustering, where the basis matrix represents the cluster centroids.
Xiaohua Xu, Ping He
openaire   +2 more sources

Constrained Clustering With Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems, 2016
Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available.
Xianchao, Zhang, Linlin, Zong, Xinyue, Liu, Jiebo, Luo   +3 more
openaire   +2 more sources

Virtual label constraint Nonnegative Matrix Factorization

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
This paper proposes a novel Semi-supervised Nonnegative Matrix Factorization (NMF), called Virtual Label Constraint Nonnegative Matrix Factorization (VLCNMF). The idea of the VLCNMF is to extend the NMF by incorporating a virtual label constraint into the NMF decomposition.
null Xiaobing Pei, null Changqing Chen, null Weihua Gong   +2 more
openaire   +1 more source

Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks

Multimedia tools and applications, 2022
E. Nasiri, K. Berahmand, Yuefeng Li
semanticscholar   +1 more source

Deep asymmetric nonnegative matrix factorization for graph clustering

Pattern Recognition, 2023
Akram Hajiveiseh, Seyed Amjad Seyedi, F. Tab   +2 more
semanticscholar   +1 more source