Results 291 to 300 of about 2,535,217 (348)

Spectral-Spatial Hyperspectral Unmixing Using Nonnegative Matrix Factorization

IEEE Transactions on Geoscience and Remote Sensing, 2021
Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components ...
Shaoquan Zhang, Guorong Zhang, Fan Li, Chengzhi Deng, Shengqian Wang, A. Plaza, Jun Li   +6 more
semanticscholar   +1 more source

Bicriteria Sparse Nonnegative Matrix Factorization via Two-Timescale Duplex Neurodynamic Optimization

IEEE Transactions on Neural Networks and Learning Systems, 2021
In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation ...
Hangjun Che, Jun Wang, A. Cichocki
semanticscholar   +1 more source

Factor-Bounded Nonnegative Matrix Factorization

ACM Transactions on Knowledge Discovery from Data, 2021
Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of ...
Kai Liu, Xiangyu Li, Zhihui Zhu, Lodewijk Brand, Hua Wang   +4 more
semanticscholar   +1 more source

Nonnegative matrix factorization: When data is not nonnegative

2014 7th International Conference on Biomedical Engineering and Informatics, 2014
In this paper, we present a new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values. When a NMF problem is formulated as μ ≈μμ, we try to develop a new method that only allows μ to contain nonnegative values, but allows both μ and μ to have both nonnegative and negative values. In this
Siyuan Wu, Jim Wang
openaire   +1 more source

Nonnegative Discriminant Matrix Factorization

IEEE Transactions on Circuits and Systems for Video Technology, 2017
Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification.
Yuwu Lu, Zhihui Lai, Xuelong Li
exaly   +3 more sources

Quadratic nonnegative matrix factorization

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
openaire   +5 more sources

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

Labelwalking nonnegative matrix factorization

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion.
Long Lan, Naiyang Guan, Xiang Zhang 0008, Xuhui Huang, Zhigang Luo   +4 more
openaire   +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

Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey

arXiv.org
Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data.
Farid Saberi-Movahed, Kamal Berahmand, Razieh Sheikhpour, Yuefeng Li, Shirui Pan   +4 more
semanticscholar   +1 more source