Suchergebnisse - machinery learning matrix factorization algorithm~

Separation and Extraction of Compound-Fault Signal Based on Multi-Constraint Non-Negative Matrix Factorization von Wang, Mengyang, Zhang, Wenbao, Shao, Mingzhen, Wang, Guang

“… To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed …”
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A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization von Wang, Huaqing, Wang, Mengyang, Li, Junlin, Song, Liuyang, Hao, Yansong

“… In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF …”
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IBL-AE: An interpretable base learning autoencoder for intelligent fault diagnosis of rotating machinery von Li, Hongkun, Yang, Chen, Han, Bo, Cao, Xiaoyu

“… IBL-AE incorporates a non-negative decomposition module inspired by non-negative matrix factorization (NMF …”
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The matrix ridge approximation: algorithms and applications von Zhang, Zhihua

“… We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods …”
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Novel indicators for monitoring bearing condition using frequency-domain dictionary learning von Yun, Hannah, Giurcăneanu, Ciprian Doru, Dobbie, Gillian

“… The differences between the log-spectra of past segments and the current segment are stored as the columns of a matrix, which is factorized using dictionary learning (DL …”
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ISLU: Indexing-Efficient Sparse LU Factorization for Circuit Simulation on GPUs (Invited Paper) von Niu, Dan, Tao, Yiyang, Jin, Zhou, Dong, Yichao, Wang, Chao, Sun, Changyin

“… Conventional LU factorization methods generally involve two approaches: either they utilize space-intensive dense matrices for direct index-to-data mapping, or they inefficiently scour through indices to locate the positions of updated data elements …”
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MatFactory: A Framework for High-Performance Matrix Factorization on FPGAs von Zhang, Mingzhe, Hao, Xiaochen, Rong, Hongbo, Chen, Wenguang

“… Matrix factorization is a widely used powerful tool in signal processing, machine learning and high performance computing …”
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Non-negative EMD manifold for feature extraction in machinery fault diagnosis von Wang, Cong, Gan, Meng, Zhu, Chang’an

“… The first step employs non-negative matrix factorization (NMF) on IMFs selected by correlation analysis, and then extracts NNE features by optimization algorithms …”
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Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization von Xiaohua Shi, Hongtao Lu, Yangchen He, Shan He

“… Non-negative Matrix Factorization (NMF) aims to find two non-negative matrices whose product approximates the original matrix well, and is widely used in clustering condition with good physical interpretability and universal applicability …”
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Spatiotemporal non-negative projected convolutional network with bidirectional NMF and 3DCNN for remaining useful life estimation of bearings von Wang, Xu, Wang, Tianyang, Ming, Anbo, Zhang, Wei, Li, Aihua, Chu, Fulei

“… •The proposed BiNMF algorithm can strongly support the efficient operation of the TFR-based prognostics models …”
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Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning von Sun, Guodong, Hu, Ye, Wu, Bo, Zhou, Hongyu

“… Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively …”
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Inference for Ever-Changing Policy of Taint Analysis von Chiang, Wen-Hao, Li, Peixuan, Zhou, Qiang, Banerjee, Subarno, Schaef, Martin, Lyu, Yingjun, Nguyen, Hoan, Tripp, Omer

“… Identifying correct and complete taint specifications is critical for detecting vulnerabilities in the ever-changing landscape of software security, and an …”
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Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm von Lim, Hansaim, Xie, Lei

“… Here, we developed a new one-class collaborative filtering algorithm tREMAP that is based on regularized, weighted nonnegative matrix tri-factorization …”
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Low-Rank Sinkhorn Factorization von Meyer Scetbon, Cuturi, Marco, Peyré, Gabriel

“… Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm …”
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Social Trust Prediction Using Heterogeneous Networks von Huang, Jin, Nie, Feiping, Huang, Heng, Tu, Yi-Cheng, Lei, Yu

“… Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter …”
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Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients von Shen, Hong-yu, Xu, Jia-lin, Zhu, Zhen, Xu, Hai-ping, Liang, Ming-xing, Xu, Di, Chen, Wen-quan, Tang, Jin-hai, Fang, Zheng, Zhang, Jian

“… ) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF …”
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Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning von Hu, Ye, Lin, Kai, Gao, Yuan, Sun, Guodong

“… Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC …”
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Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors von Wang, Yida, Anderson, Michael J., Cohen, Jonathan D., Heinecke, Alexander, Li, Kai, Satish, Nadathur, Sundaram, Narayanan, Turk-Browne, Nicholas B., Willke, Theodore L.

“… Full correlation matrix analysis (FCMA) is an unbiased approach for exhaustively studying interactions among brain regions in functional magnetic resonance imaging (fMRI …”
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SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping von Perros, Ioakeim, Papalexakis, Evangelos E, Park, Haesun, Vuduc, Richard, Yan, Xiaowei, Defilippi, Christopher, Stewart, Walter F, Sun, Jimeng

“… This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers …”
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A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers von Kißkalt, Dominik, Fleischmann, Hans, Kreitlein, Sven, Knott, Manuel, Franke, Jörg

“… Implementing condition monitoring functionality in production machinery often proves to be a difficult task …”
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