Suchergebnisse - Machine learning Matrix factorization Algorithm

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis von Mou, Lisha, Lu, Ying, Wu, Zijing, Pu, Zuhui, Huang, Xiaoyan, Wang, Meiying

“… In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF …”
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On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data von Devarajan, Karthik, Cheung, Vincent C K

“… Nonnegative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H, where V ~ WH …”
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Double feature selection algorithm based on low-rank sparse non-negative matrix factorization von Shang, Ronghua, Song, Jiuzheng, Jiao, Licheng, Li, Yangyang

“… Recently, many feature selection algorithms based on non-negative matrix factorization have been proposed …”
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SGRiT: Non-Negative Matrix Factorization via Subspace Graph Regularization and Riemannian-Based Trust Region Algorithm von Nokhodchian, Mohsen, Moattar, Mohammad Hossein, Jalali, Mehrdad

“… Non-negative Matrix Factorization (NMF) has gained popularity due to its effectiveness in clustering and feature selection tasks …”
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LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates von Wang, Guoli, Kossenkov, Andrew V, Ochs, Michael F

“… Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data …”
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Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis von Rao, Li, Peng, Bo, Li, Tao

“… The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm …”
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Revisiting data reduction for boolean matrix factorization algorithms based on formal concept analysis von Yang, Lanzhen, Tsang, Eric C. C., Mao, Hua, Zhang, Chengling, Wu, Jiaming

“… Boolean Matrix Factorization (BMF) helps unveil hidden patterns in boolean datasets and is a powerful tool in machine learning …”
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Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and Stimulus Token von Bauer, Sydney W, Jeng, Fuh-Cherng, Carriero, Amanda

“… While the FFR has been studied extensively, there are limitations in obtaining and analyzing FFR recordings that recent machine learning algorithms may address …”
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A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint von Kimura, Keigo, Kudo, Mineichi, Tanaka, Yuzuru

“… Recently orthogonal nonnegative matrix factorization (ONMF), imposing an orthogonal constraint into NMF, has been attracting a great deal of attention …”
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Non-negative matrix factorization reveals seasonal and sex-specific metabolic tactics in tropical tilapia von Wang, Min-Chen, Wang, Ching-Wei, Furukawa, Fumiya, Lin, Ching-Chun, Lin, Tzu-Hao, Tseng, Yung-Che

“… We utilized Non-negative Matrix Factorization (NMF), an unsupervised machine learning algorithm, to analyze monthly serum metabolomics data from tilapia over a year in order to overcome these limits …”
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Tri-regularized nonnegative matrix tri-factorization for co-clustering von Deng, Ping, Li, Tianrui, Wang, Hongjun, Horng, Shi-Jinn, Yu, Zeng, Wang, Xiaomin

“… The nonnegative matrix tri-factorization (NMTF) algorithm, which aims to decompose an objective matrix into three low-dimensional matrices, is an important tool to achieve co-clustering …”
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Distributed Differentially Private Algorithms for Matrix and Tensor Factorization von Imtiaz, Hafiz, Sarwate, Anand D.

“… In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms …”
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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering von Deng, Ping, Li, Tianrui, Wang, Hongjun, Wang, Dexian, Horng, Shi-Jinn, Liu, Rui

“… The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining …”
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Stokes shift spectroscopy and machine learning for label-free human prostate cancer detection von Pu, Yang, Wu, Binlin, Mo, Haiding, Alfano, Robert R

“… ) algorithms, including principal component analysis (PCA), nonnegative matrix factorization (NMF …”
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Privacy-preserving non-negative matrix factorization for decentralized-data using correlated noise von Imtiaz, Hafiz, Karmakar, Tusher, Mohanta, Protoye Kumar

“… Several matrix factorization algorithms are employed in machine learning applications …”
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Dimension Reduction With Extreme Learning Machine von Kasun, Liyanaarachchi Lekamalage Chamara, Yang, Yan, Huang, Guang-Bin, Zhang, Zhengyou

“… Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms …”
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A Matrix Factorization Algorithm for Efficient Recommendations in Social Rating Networks Using Constrained Optimization von Ampazis, Nicholas, Emmanouilidis, Theodoros, Sakketou, Flora

“… In recent years the emergence of social media has become more prominent than ever. Social networking has become the de facto tool used by people all around the …”
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Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey von Jiang, Shuo, Kang, Peiqi, Song, Xinyu, Lo, Benny, Shull, Peter

“… The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control …”
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Online Simplex-Structured Matrix Factorization von Kouakou, Hugues, Goulart, Jose Henrique de Morais, Vitale, Raffaele, Oberlin, Thomas, Rousseau, David, Ruckebusch, Cyril, Dobigeon, Nicolas

“… Simplex-structured matrix factorization (SSMF) is a common task encountered in signal processing and machine learning …”
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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy von Huang, Shenjun, Tu, Tingting

“… We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models …”
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