Results 261 to 270 of about 62,622 (323)

Amplitude filtering characteristics of singular value decomposition and its application to fault diagnosis of rotating machinery

Measurement: Journal of the International Measurement Confederation, 2020
In this paper, two important properties of singular value decomposition (SVD) are deduced theoretically: (1) number law of singular values: one frequency corresponds to two singular values; (2) order rule of singular values: the larger the amplitude of ...
Mingjun Guo, Xuezhi Zhao
exaly   +2 more sources

SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

International Conference on Learning Representations
The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitates LLM compression methods for practical deployment.
Xin Wang, Yu Zheng, Zhongwei Wan, Mi Zhang   +3 more
semanticscholar   +3 more sources

Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

International Journal of Information Technology and Decision Making, 2021
In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start.
Taushif Anwar, V. Uma, Gautam Srivastava
semanticscholar   +1 more source

Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum

, 2021
The diagnosis of early-stage defects of rolling element bearings (REBs) using vibration signals is a very difficult task since bearing fault signals are usually weak and masked by shaft rotating signals, gear meshing signals, and strong background noise.
Lang Xu, S. Chatterton, P. Pennacchi
semanticscholar   +1 more source

A Tutorial on Singular Value Decomposition with Applications on Image Compression and Dimensionality Reduction

2021 International Conference on Information Technology (ICIT), 2021
This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as principal component analysis (PCA), eigen decomposition, matrix ...
Yousef Jaradat, M. Masoud, Ismael Jannoud, Ahmad M. Manasrah, M. Alia   +4 more
semanticscholar   +1 more source

Neural Embedding Singular Value Decomposition for Collaborative Filtering

IEEE Transactions on Neural Networks and Learning Systems, 2021
Singular value decomposition (SVD) is one of the most effective algorithms in recommender systems (RSs). Due to the iterative nature of SVD algorithms, one big challenge is initialization that has a major impact on the convergence and performance of RSs.
Tianlin Huang, Rujie Zhao, Lvqing Bi, Defu Zhang, Chao Lu   +4 more
semanticscholar   +1 more source

Robust tensor completion using transformed tensor singular value decomposition

Numerical Linear Algebra with Applications, 2020
In this article, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD.
Guang-Jing Song, M. Ng, Xiongjun Zhang
semanticscholar   +1 more source

Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels

Biocybernetics and Biomedical Engineering, 2020
In this study, a novel method to automatically detect Parkinson's disease (PD) using vowels is proposed. A combination of minimum average maximum (MAMa) tree and singular value decomposition (SVD) are used to extract the salient features from the voice ...
Turker Tuncer, M. Kutlu Sengul, U. Acharya   +2 more
semanticscholar   +1 more source

Analysis of scintigrams by singular value decomposition (SVD) technique

Annals of Nuclear Medicine, 1994
The singular value decomposition (SVD) method is presented as a potential tool for analyzing gamma camera images. Mathematically image analysis is a study of matrixes as the standard scintigram is a digitized matrix presentation of the recorded photon fluence from radioactivity of the object.
S E, Savolainen, B K, Liewendahl
openaire   +2 more sources

Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform

IEEE Transactions on Instrumentation and Measurement, 2020
Online partial discharge (PD) monitoring is an important means to detect insulation deterioration. However, it is difficult to extract the PD signal due to various interferences in the field.
Jun Zhong, Xiaowen Bi, Qin Shu, Minwei Chen, Dianbo Zhou, Dakun Zhang   +5 more
semanticscholar   +1 more source