Search Results - machinery learning matrix factorization algorithm*

Separation and Extraction of Compound-Fault Signal Based on Multi-Constraint Non-Negative Matrix Factorization by 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…”
Get full text

A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization by 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…”
Get full text

The matrix ridge approximation: algorithms and applications by 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…”
Get full text

Novel indicators for monitoring bearing condition using frequency-domain dictionary learning by 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…”
Get full text

MatFactory: A Framework for High-Performance Matrix Factorization on FPGAs by 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…”
Get full text

ISLU: Indexing-Efficient Sparse LU Factorization for Circuit Simulation on GPUs (Invited Paper) by 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…”
Get full text

IBL-AE: An interpretable base learning autoencoder for intelligent fault diagnosis of rotating machinery by Li, Hongkun, Yang, Chen, Han, Bo, Cao, Xiaoyu

“… IBL-AE incorporates a non-negative decomposition module inspired by non-negative matrix factorization (NMF…”
Get full text

Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization by 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…”
Get full text

Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning by 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…”
Get full text

Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm by 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…”
Get more information

Inference for Ever-Changing Policy of Taint Analysis by 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…”
Get full text

Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning by Hu, Ye, Lin, Kai, Gao, Yuan, Sun, Guodong

“… Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC…”
Get full text

Low-Rank Sinkhorn Factorization by 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…”
Get full text

Non-negative EMD manifold for feature extraction in machinery fault diagnosis by 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…”
Get full text

Exploration of methodologies to improve job recommender systems on social networks by Diaby, Mamadou, Viennet, Emmanuel, Launay, Tristan

“…This paper presents content-based recommender systems which propose relevant jobs to Facebook and LinkedIn users. These systems have been developed at Work4,…”
Get full text

Spatiotemporal non-negative projected convolutional network with bidirectional NMF and 3DCNN for remaining useful life estimation of bearings by 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…”
Get full text

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors by 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…”
Get full text

Fast Component Pursuit for Large-Scale Inverse Covariance Estimation by Han, Lei, Zhang, Yu, Zhang, Tong

“…, which is assumed to be a combination of a low-rank part and a diagonal matrix. One motivation for this assumption is that the low-rank structure is common in many applications…”
Get more information

Robust and Scalable Algorithms for Bayesian Nonparametric Machine Learning by Roychowdhury, Anirban

“… However the richness comes at the cost of significant complexity of learning and inference for large scale datasets, in addition to an orthogonal set of challenges related to algorithm convergence and correctness…”
Get full text

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients by 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…”
Get full text