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dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

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Journal of Data and Information Science 2023年第4期8卷 102-124页

作者： Wei Cui Zhiqiang Xu Ren Mu School of Statistics Jilin University of Finance and EconomicsChangchunJilin 130117China

Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretability of these models presents a persistent challenge.Design/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class centers.In contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.Findings:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life expectancy.The findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring compactness.It also maintains compatibility with other models.Additionally,DRMBIP-v finds the optimal result,showing exceptional separation.Visualization of the results reveals that all classes have a high compactness.Research limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction results.In the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or compactness.This necessitates an artificial adjustment to the overflow component within the objective function.Practical implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators.Validated by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data dimensions.Originality/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering.It not only has applications in life expec

关键词： Integer programming Multidimensional data dimensionality reduction Life expectancy

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AE-TPGG:a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction

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Frontiers of Computer Science 2023年第3期17卷 217-234页

作者： Shuchang ZHAO Li ZHANG Xuejun LIU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Computer Science and Technology Nanjing Forestry UniversityNanjing 210037China

Single-cell RNA sequencing(scRNA-seq)technology has become an effective tool for high-throughout transcriptomic study,which circumvents the averaging artifacts corresponding to bulk RNA-seq technology,yielding new perspectives on the cellular diversity of potential superficially homogeneous populations.Although various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material,the technical noise and biological variation are inevitably introduced into experimental process,resulting in high dropout events,which greatly hinder the downstream analysis.Considering the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data,we propose a customized autoencoder based on a twopart-generalized-gamma distribution(AE-TPGG)for scRNAseq data analysis,which takes mixed discrete-continuous random variables of scRNA-seq data into account using a twopart model and utilizes the generalized gamma(GG)distribution,for fitting the positive and right-skewed continuous data.The adopted autoencoder enables AE-TPGG to captures the inherent relationship between genes.In addition to the ability of achieving low-dimensional representation,the AETPGG model also provides a denoised imputation according to statistical characteristic of gene expression.Results on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.

关键词： scRNA-seq autoencoder TPGG data imputation dimensionality reduction

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dimensionality reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

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Computer Systems Science & Engineering 2023年第11期47卷 2481-2496页

作者： S.Srinivasan K.Rajakumar School of Computer Science and Engineering VIT UniversityVellore632014India

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

关键词： Hyperspectral image dimensionality reduction depth-wise separable model water strider optimization self-organized map

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A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

Energy and AI

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Energy and AI 2023年第4期14卷 319-328页

作者： Devinder Kaur Shama Naz Islam MdApel Mahmud Md.Enamul Haque Adnan Anwar School of Engineering Deakin UniversityAustralia Faculty of Engineering and Environment Northumbria UniversityNewcastle Upon TyneUK School of Information Technology Deakin UniversityStrategic Centre for Cyber Security Research Institute(CSRI)Geelong 3216Australia

The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.

关键词： Bayesian deep learning Bidirectional long-short term memory dimensionality reduction Generation forecasting Renewable power generation Variational auto-encoders

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Rough Sets Hybridization with Mayfly Optimization for dimensionality reduction

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Computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： Ahmad Taher Azar Mustafa Samy Elgendy Mustafa Abdul Salam Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer Studies Arab Open UniversityCairoEgypt Faculty of Information Technology and Computer Science Nile UniversityShikh ZaidEgypt

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis.dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily.These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues.To achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in particular.The performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the literature.The simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data sets.Finally,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data problem.MA-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.

关键词： dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection

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dimensionality reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized Auto-encoder

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Chinese Journal of Electronics 2017年第6期26卷 1233-1238页

作者： WANG Xuesong KONG Yi CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology

To achieve high classification accuracy of hyperspectral data, a dimensionality reduction algorithm called Sample-dependent repulsion graph regularized auto-encoder(SRGAE) is proposed. Based on the sample-dependent graph, by applying the repulsion force to the samples from different classes but nearby, a sampledependent repulsion graph is built to make the samples from the same class will be pro jected to samples that are close-by and the samples from different classes will be pro jected to samples that are far away. The sampledependent repulsion graph can avoid the neighborhood parameter selection problem existing in the nearest neighborhood graph. By integrating advantages of deep learning and graph regularization technique, the SRGAE can maintain the learned deep features are consistent with the inherent manifold structure of the original hyperspectral data. Experimental results on two real hyperspectral data show that, when compared with some popular dimensionality reduction algorithms, the proposed SRGAE can yield higher classification accuracy.

关键词： Hyperspectral data dimensionality reduction Sample-dependent repulsion graph Regularized Auto-encoder

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dimensionality reduction with adaptive graph

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Frontiers of Computer Science 2013年第5期7卷 745-753页

作者： Lishan QIAO Limei ZHANG Songcan CHEN Department of Mathematics Science Liaocheng University Liaocheng 252000 China Department of Computer Science and Engineering Nanjing University of Aeronautics & Astronautics Nanjing 210016 China

Graph-based dimensionality reduction （DR） methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is inde- pendent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an al- gorithm called dimensionality reduction with adaptive graph （DRAG）, whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a pre- specified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update for- mula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.

关键词： dimensionality reduction graph construction face recognition

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dimensionality reduction of hyperspectral images of vegetation and crops based on self-organized maps

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Information Processing in Agriculture 2021年第2期8卷 310-327页

作者： David Ruiz Hidalgo Bladimir Bacca Cortés Eduardo Caicedo Bravo Perception and Intelligent Systems Research Group School of Electrical and Electronic EngineeringUniversidad del ValleSantiago de Cali 76001Colombia

Hyperspectral images are multidimensional massive sets of information that have shown a great potential for different kind of applications as urban mapping,environmental management,vegetation and crops supervision and mineral detection.However,due to its high dimensional nature and the high variability of the spectral information,the dimensionality reduction process is one of the main challenges in processing hyperspectral images.The aim of dimensionality reduction is to eliminate redundant information and simplify the subsequent processes of classification and the search of information.In this context,several dimensionality reduction methods have been proposed,but most of them are not flexible enough to deal with the particular features of the hyperspectral images.In this way,the use of intelligent methods as neural networks and specially an unsupervised approach as self-organized maps,may improve the dimensionality reduction stage and the final classification process.This paper proposes an unsupervised method for the dimensionality reduction of hyperspectral images based on Kohonen self-organized maps,which,compared with other traditional methods such as principal component analysis(PCA)and wavelet decomposition,provides better classification results.The results provided in this paper use an RBF(radial basis function)classifier.On average,the proposed method provides a 64%dimensionality reduction and an 88.5%classification accuracy.These results suggest that the dimensionality reduction algorithm based on self-organized maps is an efficient approach compared with other popular algorithms.This is due to the ability of self-organized maps to automatically detect(self-organizing)relationships within the set of input patterns,which provides flexibility to deal with the special features of the hyperspectral images.

关键词： dimensionality reduction Hyperspectral images Principal component analysis Radial basis function Remote sensing Self-organized maps Wavelet transform

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dimensionality reduction by Mutual Information for Text Classification

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Journal of Beijing Institute of Technology 2005年第1期14卷 32-36页

作者：刘丽珍宋瀚涛陆玉昌 Information Engineering College Capital Normal University Beijing100037 China School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Laboratory

The frame of text classification system was presented. The high dimensionality in feature space for text classification was studied. The mutual information is a widely used information theoretic measure, in a descriptive way, to measure the stochastic dependency of discrete random variables. The measure method was used as a criterion to reduce high dimensionality of feature vectors in text classification on Web. Feature selections or conversions were performed by using maximum mutual information including linear and non-linear feature conversions. Entropy was used and extended to find right features commendably in pattern recognition systems. Favorable foundation would be established for text classification mining.

关键词： text classification mutual information dimensionality reduction

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Spatial weight matrix in dimensionality reduction reconstruction for microelectromechanical system-based photoacoustic microscopy

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Visual Computing for Industry,Biomedicine,and Art 2020年第1期3卷 247-256页

作者： Yuanzheng Ma Chang Lu Kedi Xiong Wuyu Zhang Sihua Yang MOE Key Laboratory of Laser Life Science&Institute of Laser Life Science College of BiophotonicsSouth China Normal UniversityGuangzhou 510631China Guangdong Provincial Key Laboratory of Laser Life Science College of BiophotonicsSouth China Normal UniversityGuangzhou 510631China

A micro-electromechanical system(MEMS)scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy(OR-PAM).However,the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image.Moreover,the size of the airy disk,ultrasonic sensor properties,and thermal effects decrease the resolution.Thus,in this study,we proposed a spatial weight matrix(SWM)with a dimensionality reduction for image reconstruction.The three-layer SWM contains the invariable information of the system,which includes a spatial dependent distortion correction and 3D deconvolution.We employed an ordinal-valued Markov random field and the Harris Stephen algorithm,as well as a modified delay-and-sum method during a time reversal.The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM;this is also true for severely distorted images.The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index,on average.Moreover,the peak signal-to-noise ratio was increased by 17.08%after 3D deconvolution.This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

关键词： Photoacoustic microscopy Spatial weight matrix dimensionality reduction Distortion correction Mutual information

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