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Fast Dimension Reduction Based on NMF
2010Non-negative matrix factorization is an important method in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One of its significant drawback lies in its computational complexity.
Pavel Krömer +2 more
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Deep Convex NMF for Image Clustering
2016Conventional matrix factorization methods fail to exploit useful information from rather complex data due to their single-layer structure. In this paper, we propose a novel deep convex non-negative matrix factorization method (DCNMF) to improve the ability of feature representation.
Bin Qian 0006 +3 more
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β-divergence NMF with biorthogonal regularization for data representation
Engineering Applications of Artificial Intelligence, 2023Haoliang Yuan, Chengcai Leng, Bing Li
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NMF-based approach to automatic term extraction
Expert Systems With Applications, 2022Aliya Nugumanova +2 more
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Multiplicative Updates for NMF with $\beta$-Divergences under Disjoint Equality Constraints
SIAM Journal on Matrix Analysis and Applications, 2021Valentin Leplat +2 more
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Detecting cell assemblies by NMF-based clustering from calcium imaging data
Neural Networks, 2022Toshimitsu Aritake +2 more
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NMF: A Multi-platform Modeling Framework
2018For its promises in terms of increased productivity, Model-driven engineering (MDE) is getting applied increasingly often in both industry and academia. However, most tools currently available are based on the Eclipse Modeling Framework (EMF) and hence based on the Java platform whereas tool support for other platforms is limited.
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Label consistency-based deep semisupervised NMF for tumor recognition
Engineering Applications of Artificial Intelligence, 2023Xiaohui Yang
exaly
Improving NMF clustering by leveraging contextual relationships among words
Neurocomputing, 2022Aghiles Salah, Mohamed Nadif
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