Results 231 to 240 of about 55,457 (295)

Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation

IEEE Transactions on Neural Networks and Learning Systems, 2020
Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled.
Yang Meng, Ronghua Shang, Fanhua Shang
exaly   +2 more sources

Experimental explorations on short text topic mining between LDA and NMF based Schemes

Knowledge-Based Systems, 2019
Learning topics from short texts has become a critical and fundamental task for understanding the widely-spread streaming social messages, e.g., tweets, snippets and questions/answers.
Yong Chen
exaly   +2 more sources

A Generalized Deep Learning Algorithm Based on NMF for Multi-View Clustering

IEEE Transactions on Big Data, 2023
Multi-view clustering research is a hot topic in the field of data mining, where complementary information between views can better describe data objects and improve the clustering performance.
Dexian Wang, Tianrui Li, Ping Deng, Jia Liu, Wei Huang, Fan Zhang   +5 more
semanticscholar   +1 more source

Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2020
As one of the most important information of hyperspectral images (HSI), spatial information is usually simulated with the similarity among pixels to enhance the unmixing performance of nonnegative matrix factorization (NMF).
Xiaoqiang Lu, Yuan Yuan, Dong Le
exaly   +2 more sources

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF

Neural Information Processing Systems, 2022
This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. To this end, we propose a novel interpreter design
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alch'e-Buc, G. Richard   +4 more
semanticscholar   +1 more source

Adaptive Relationship Preserving Sparse NMF for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2022
Hyperspectral unmixing is an essential research topic for spectral data analysis due to the existence of mixed pixels. Recently, many methods based on sparse nonnegative matrix factorization (NMF) have been widely used for unmixing by incorporating ...
Xuelong Li, Xinxin Zhang, Yuan Yuan, Yongsheng Dong   +3 more
semanticscholar   +1 more source

Spectral–Spatial Joint Sparse NMF for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2021
The nonnegative matrix factorization (NMF) combining with spatial–spectral contextual information is an important technique for extracting endmembers and abundances of hyperspectral image (HSI).
Le Dong, Yuan Yuan, Xiaoqiang Luxs
semanticscholar   +1 more source

Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data

IEEE Transactions on Cybernetics, 2020
Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifold ...
Na Yu, Ming-Juan Wu, Jin-Xing Liu, C. Zheng, Yong Xu   +4 more
semanticscholar   +1 more source

Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM

IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2020
In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of ...
Andri Mirzal
semanticscholar   +1 more source