Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction by Zhaoyang Zhang, Shijie Sun, Wei Wang

“…The extracted matrix-based feature is useful to both dimension reduction and spatial statistics analysis for an image. …”
Get full text

Split-and-Combine Singular Value Decomposition for Large-Scale Matrix by Jengnan Tzeng

“…It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. …”
Get full text

Ear Recognition Based on Gabor Features and KFDA by Li Yuan, Zhichun Mu

“…Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. …”
Get full text

A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response by Hanji He, Jianfeng He, Guangming Deng

“…In the application of lung cancer diagnosis, the proposed method for imbalanced data categorization is impressive, and the dimension reduction using linear discriminant is still good.…”
Get full text

Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis by Zhigao Zeng, Zhiqiang Wen, Shengqiu Yi, Sanyou Zeng, Yanhui Zhu, Qiang Liu, Qi Tong

“…Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. …”
Get full text

Reduction of Multidimensional Image Characteristics Based on Improved KICA by Jia Dongyao, Ai Yanke, Zou Shengxiong

“…The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. …”
Get full text

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1 by Hai Guo, Jinghua Yin, Jingying Zhao, Yuanyuan Liu, Lei Yao, Xu Xia

“…The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). …”
Get full text

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods by Said Ziani, Yousef Farhaoui, Mohammed Moutaib

“…It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). …”
Get full text

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model by Changming Liu, Di Zhou, Zhigang Wang, Dan Yang, Gangbing Song

“…By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text