Research on gear reliability based on improved fourth moment by WANG ChunRong, ZHOU FuPing, XIA ErDong, GAO Hao

“…Then, the correlation between multiple failure modes and the overall failure probability considering failure correlation were calculated based on the conditional probability dimension reduction method. Finally, taking the transmission gear of a system a a example, the calculation results of the proposed method, mean value first order second moment (MVFOSM) method, advanced first order second moment (AFOSM) method and traditional high-order moment standardization technique (HOMST) method were compared. …”
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Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN by Hongmei Liu, Jiayao Jing, Jian Ma

“…Then, the principal component analysis (PCA) was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network (PNN) was utilized to classify the fault modes. …”
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Rolling Bearing Degradation State Identification Based on LPP Optimized by GA by He Yu, Hong-ru Li, Zai-ke Tian, Wei-guo Wang

“…In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. …”
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SBS Content Detection for Modified Asphalt Using Deep Neural Network by Zhixiang Wang, Jiange Li, Zhengqi Zhang, Youxiang Zuo

“…Results show that the mean square error value decreased by 68% for DNN with noise and dimension reduction. The DNN-based prediction model showed that the correlation coefficient between the target value and the mean predicted value is 0.9978 and 0.9992 for training and testing samples, respectively, indicating its remarkable accuracy and applicability after training. …”
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A novel framework for face recognition using robust local representation–based classification by Aihua Yu, Gang Li, Beiping Hou, Hongan Wang, Gaoya Zhou

“…To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. …”
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Robust and Efficient Harmonics Denoising in Large Dataset Based on Random SVD and Soft Thresholding by Yu Yang, Jian Rao

“…To overcome the computational difficulties of SVD for the big dataset, dimension reduction of the matrix is necessary, but it results in a significant reduction on signal intensities. …”
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Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm by Jinqing Zhang, Pengchao Zhang, Bin Xu

“…First, the singular value decomposition is used in pretreatment of data set which includes outlier detection and dimension reduction. Then, the genetic algorithm is introduced in the training process to find the proper initial parameters of network, and in this way, it can prevent the network from falling into the local minimum. …”
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A Time Scales Approach to Coinfection by Opportunistic Diseases by Marcos Marvá, Ezio Venturino, Rafael Bravo de la Parra

“…The primary disease acts at the slow time scale while the secondary disease does at the fast one, allowing a dimension reduction of the system and making its analysis tractable. …”
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Uncertainty Evaluation of Stochastic Structural Response with Correlated Random Variables by Qiang Fu, Jianjun Liu, Jiarui Shi, Xiao Li, Xueji Cai, Zilong Meng

“…In this method, the evaluation expression for the mean and standard deviation of the maximum response including uncertainty parameter variables are provided first; subsequently, a third-moment pseudo-correlation normal transformation is able to be performed for converting the correlated and non-normal system parameter variables with unknown joint probability density function (PDF) or marginal PDF into the mutually independent standard normal ones; ultimately, a point estimate procedure (PEP) based on univariate dimension reduction integration can be carried out for evaluating the structural stochastic response including uncertainty system parameters. …”
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Trend Analysis and Comprehensive Evaluation of Green Production Principal Component of Thermal Power Unit Based on ANP-MEEM Model by Zhongfu Tan, Qingkun Tan, Liwei Ju, Shenbo Yang, Huangfu Cheng, Jiale Ma

“…The indexes of strong contribution index and short board of barrel are found out, and the dimension reduction management of green production of thermal power unit is realized.…”
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Comparing subsampling strategies for metagenomic analysis in microbial studies using amplicon sequence variants versus operational taxonomic units. by Daniel Segura, Divya Sharma, Osvaldo Espin-Garcia

“…The aim of this work is to compare the subsampling strategies for two-phase metagenomic studies when using ASVs instead of OTUs, and to propose data driven strategies for subsample selection through dimension reduction techniques. We used 199 samples of infant-gut microbiome data from the DIABIMMUNE project to generate ASVs and OTUs, then generated subsamples based on five existing biologically driven subsampling methods and two data driven methods. …”
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Damped Iterative Explicit Guidance for Multistage Rockets with Thrust Drop Faults by Zongzhan Ma, Chuankui Wang, Zhi Xu, Shuo Tang, Ying Ma

“…Based on the iterative guidance mode (IGM) and powered explicit guidance (PEG), this method is enhanced in three aspects: (1) an accurate transversality condition is derived and applied in the dimension-reduction framework instead of using a simplified assumption; (2) the Gauss–Legendre quadrature formula (GLQF) is adopted to increase the accuracy of the method by addressing the issue of excessive errors in calculating thrust integration using linearization methods based on a small quantity assumption under fault conditions; and (3) a damping factor for solving the time-to-go is introduced to avoid the chattering phenomenon and enhance convergence. …”
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A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification by Lahbib Khrissi, Nabil El Akkad, Hassan Satori, Khalid Satori

“…In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. …”
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Comparison of Feature Selection and Feature Extraction Role in Dimensionality Reduction of Big Data by Haidar Khalid Malik, Nashaat Jasim Al-Anber, Fuad AbdoEsmail Al- Mekhlafi

“…We applied many classifiers like (Support vector machines, k-nearest neighbors, Decision tree, and Naive Bayes ) to the data of the anthropometric survey of US Army personnel (ANSUR 2) to classify the data and test the relevance of features by predicting a specific feature in USA Army personnel results showing that (k-nearest neighbors) achieved high accuracy (83%) in prediction, then reducing the dimensions by several techniques like (Highly Correlated Filter, Recursive  Feature Elimination, and principal components Analysis) results showing that (Recursive  Feature Elimination) have the best accuracy by (66%), From these results, it is clear that the efficiency of dimension reduction techniques varies according to the nature of the data. …”
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Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques by Congzhen Xiao, Baojuan Qiao, Jianhui Li, Zhiyong Yang, Jiannan Ding

“…To solve the over-fitting problem caused by the current situation of “few samples and big errors” of the experimental database, feature engineering aiming at dimension reduction is systematically carried out through an iterative process. …”
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LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks by Yuhua Zhang, Kun Wang, Min Gao, Zhiyou Ouyang, Siguang Chen

“…In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we use K -means algorithm for clustering analysis of the dimension-reduced data. …”
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Identification of LSA Data Retrieval Method and Temporal Graph for Document Retrieval by Shahla Rezvani, Nader Naghshineh, Ahmad Khalilijafarabad

“…The improved retrieval performance can be attributed to the superior performance of the dimension reduction method compared to keyword matching.…”
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Relationships between multivitamins, blood biochemistry markers, and BMC and BMD based on RF: A cross-sectional and population-based study of NHANES, 2017-2018. by Lijuan Xu, Mengqi Wu, Ying Zhang, Hongsheng Kun, Jiangbao Xu

“…<h4>Results</h4>Under the dimension reduction and comparison selection of RF model, we identified ALP, CPK, and creatinine serum concentrations as the most important factors associated with BMC and BMD in multiple skeletal sites, and the gender, age, height, weight, and body mass index which were found to be related to BMC and BMD in different skeletal sites. …”
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Sparse Deep Nonnegative Matrix Factorization by Zhenxing Guo, Shihua Zhang

“…Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. …”
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Correlation-based feature selection of single cell transcriptomics data from multiple sources by Nenad S. Mitić, Saša N. Malkov, Mirjana M. Maljković Ružičić, Aleksandar N. Veljković, Ivan Lj. Čukić, Xin Lin, Minjie Lyu, Vladimir Brusić

“…The algorithms specialized for working with high-dimensional data often cannot process data containing large data sets with several thousand dimensions (features). Dimension reduction methods (such as PCA) do not provide satisfactory results, and also obscure the meaning of the original attributes in the data. …”
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