Results 31 to 40 of about 53,454 (335)

Feature Weighted Non-Negative Matrix Factorization [PDF]

IEEE Transactions on Cybernetics, 2023
Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis. NMF concentrates the features of each sample into a vector, and approximates it by the linear combination of basis vectors, such that the low-dimensional representations ...
Mulin Chen, Maoguo Gong, Xuelong Li 0001
openaire   +3 more sources

Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization [PDF]

IEEE/ACM Transactions on Audio Speech and Language Processing, 2023
This article tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that ...
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, G. Richard, Florence d'Alch'e-Buc   +4 more
semanticscholar   +1 more source

On Affine Non-Negative Matrix Factorization [PDF]

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
We generalize the non-negative matrix factorization (NMF) generative model to incorporate an explicit offset. Multiplicative estimation algorithms are provided for the resulting sparse affine NMF model. We show that the affine model has improved uniqueness properties and leads to more accurate identification of mixing and sources.
Hans Laurberg, Lars Kai Hansen
openaire   +1 more source

Non-negative Matrix Factorization on Manifold [PDF]

2008 Eighth IEEE International Conference on Data Mining, 2008
Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed
Deng Cai 0001, Xiaofei He 0001, Xiaoyun Wu, Jiawei Han 0001   +3 more
openaire   +1 more source

Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

Frontiers in Immunology, 2021
A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug
Xianfang Tang, Lijun Cai, Yajie Meng, Junlin Xu, Changcheng Lu, Jialiang Yang   +5 more
semanticscholar   +1 more source

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

Nature Communications, 2023
The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making.
Shaoqiang Han, Qian Cui, Ruiping Zheng, Shuying Li, Bingqian Zhou, Keke Fang, Wei Sheng, B. Wen, Liang Liu, Yarui Wei, Huafu Chen, Yuan Chen, Jingliang Cheng, Yong Zhang   +13 more
semanticscholar   +1 more source

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

Briefings Bioinform., 2022
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field.
Ryuji Hamamoto, Ken Takasawa, Hidenori Machino, Kazuma Kobayashi, Satoshi Takahashi, Amina Bolatkan, Norio Shinkai, Akira Sakai, R. Aoyama, Masayoshi Yamada, Ken Asada, M. Komatsu, Koji Okamoto, H. Kameoka, Syuzo Kaneko   +14 more
semanticscholar   +1 more source

Shifted Non-Negative Matrix Factorization [PDF]

2007 IEEE Workshop on Machine Learning for Signal Processing, 2007
Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be ...
Mørup, Morten; id_orcid 0000-0003-4985-4368, Madsen, Kristoffer Hougaard; id_orcid 0000-0001-8606-7641, Hansen, Lars Kai   +2 more
openaire   +1 more source

Bayesian Non-negative Matrix Factorization [PDF]

, 2009
We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates.
Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen   +2 more
openaire   +1 more source

Rank selection for non‐negative matrix factorization

Statistics in Medicine, 2023
Non‐Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non‐negative data matrix into two lower dimensional non‐negative matrices: one is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the projections of data points to the new basis.
Yun Cai, Hong Gu, Toby Kenney
openaire   +4 more sources