Results 311 to 320 of about 2,535,217 (348)

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

Interpretable nonnegative matrix decompositions

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008
A matrix decomposition expresses a matrix as a product of at least two factor matrices. Equivalently, it expresses each column of the input matrix as a linear combination of the columns in the first factor matrix. The interpretability of the decompositions is a key issue in many data-analysis tasks. We propose two new matrix-decomposition problems: the
Saara Hyvönen, Pauli Miettinen, Evimaria Terzi   +2 more
openaire   +1 more source

Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization

IEEE Transactions on Knowledge and Data Engineering, 2019
Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks.
Xiaoke Ma, Di Dong, Quan Wang
semanticscholar   +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

Nonsmooth nonnegative matrix factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an ...
Alberto Pascual-Montano, JOSÉ Maria Carazo, Kieko Kochi   +2 more
exaly   +3 more sources

Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection

Isprs Journal of Photogrammetry and Remote Sensing, 2020
In the paper, we propose a deep nonsmooth nonnegative matrix factorization (nsNMF) network with semi-supervised learning for synthetic aperture radar (SAR) image change detection.
Hengchao Li, Gang Yang, Wen Yang, Q. Du, W. Emery   +4 more
semanticscholar   +1 more source

Robust nonnegative matrix factorization with structure regularization

Neurocomputing, 2020
Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning.
Qi Huang, Xuesong Yin, Songcan Chen, Yigang Wang, Bo Chen   +4 more
semanticscholar   +1 more source

Low-rank nonnegative matrix factorization on Stiefel manifold

Information Sciences, 2020
Low rank is an important but ill-posed problem in the development of nonnegative matrix factorization (NMF) algorithms because the essential information is often encoded in a low-rank intrinsic data matrix, whereas noise and outliers are contained in a ...
Ping He, Xiao-hua Xu, Jie Ding, Baichuan Fan   +3 more
semanticscholar   +1 more source

Structural Deep Nonnegative Matrix Factorization for community detection

Applied Soft Computing, 2020
Due to the important role in analyzing the topological structure of complex networks, community detection has attracted increasing attention recently. The network embedding methods have shown promising performances in community detection, aiming to learn
Min Zhang, Zhiping Zhou
semanticscholar   +1 more source

Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection

International Conference on Information and Knowledge Management, 2018
Community structure is ubiquitous in real-world complex networks. The task of community detection over these networks is of paramount importance in a variety of applications.
Fanghua Ye, Chuan Chen, Zibin Zheng
semanticscholar   +1 more source