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dimensionality reduction 2

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Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 569-575 doi: 10.1007/s11709-021-0735-7

Abstract: ., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”.

Keywords： digital age     urban design     multiple objectives     human-computer interaction     pan-dimensionality     individual-ubiquity    

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Fuel optimal control of parallel hybrid electric vehicles

PU Jinhuan, YIN Chenliang, ZHANG Jianwu

Frontiers of Mechanical Engineering 2008, Volume 3, Issue 3,   Pages 337-342 doi: 10.1007/s11465-008-0057-7

Abstract: To overcome the problem of numerical DP dimensionality, an algorithm to restrict the exploring region

Keywords： mathematical     Comparison     computational complexity     dimensionality     corresponding    

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A MATLAB code for the material-field series-expansion topology optimization method

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 3,   Pages 607-622 doi: 10.1007/s11465-021-0637-3

Abstract: description and the finite element discretization, and greatly reduces the number of design variables after dimensionality

Keywords： implementation     topology optimization     material-field series-expansion method     bounded material field     dimensionality    

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Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Engineering 2021, Volume 7, Issue 12,   Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028

Abstract:

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.

Keywords： Multivariate regression methods     Reduced rank regression     Sparsity     Dimensionality reduction     Variable    

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 224-238 doi: 10.1007/s11709-022-0812-6

Abstract: The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.

Keywords： soil consolidation coefficient     machine learning     random forest     Relief    

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Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1451-1478 doi: 10.1631/FITEE.2100569

Abstract:

For optimal results, retrieving a relevant feature from a has become a hot topic for researchers involved in the study of (FS) techniques. The aim of this review is to provide a thorough description of various, recent FS techniques. This review also focuses on the techniques proposed for s to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on s. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation, in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

Keywords： Feature selection     High dimensionality     Learning techniques     Microarray dataset    

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Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Strategic Study of CAE 2010, Volume 12, Issue 3,   Pages 77-81

Abstract: experiments show that the method gets higher recognition rate than other linear and kernel-based nonlinear dimensionality

Keywords： manifold learning     Laplacian eigenmaps     nonlinear dimensionality reduction     low dimensional manifold     MMW    

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The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Strategic Study of CAE 2009, Volume 11, Issue 2,   Pages 82-87

Abstract:

The data dimensionality reduction is the main method that can enhanceLocal Linear Embedding algorithm (LLE) is an effective technique for nonlinear dimensionality reductionCompared with other dimensionality reduction algorithms, the advantage of the local Linear EmbeddingEmbedding, the algorithm can select optimal parameter and regulate the distance among data set after data dimensionality

Keywords： manifold learning     detection of outliers     high dimensional data     dimensionality reduction     outliers    

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A new feature selection method for handling redundant information in text classification None

You-wei WANG, Li-zhou FENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 221-234 doi: 10.1631/FITEE.1601761

Abstract: Feature selection is an important approach to dimensionality reduction in the field of text classification

Keywords： Feature selection     Dimensionality reduction     Text classification     Redundant features     Support vector machine    

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Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation None

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1352-1361 doi: 10.1631/FITEE.1601512

Abstract: As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original featuresDDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality

Keywords： Tool condition monitoring     Manifold learning     Dimensionality reduction     Diffusion mapping analysis     Intrinsic    

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Title Author Date Type Operation

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Journal Article

Fuel optimal control of parallel hybrid electric vehicles

PU Jinhuan, YIN Chenliang, ZHANG Jianwu

Journal Article

A MATLAB code for the material-field series-expansion topology optimization method

Journal Article

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Journal Article

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

Journal Article

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Journal Article

Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Journal Article

The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Journal Article

A new feature selection method for handling redundant information in text classification

You-wei WANG, Li-zhou FENG

Journal Article

Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Journal Article