Dimensionality Reduction Technology Research Articles

Combining Prediction Models and Dimensionality Reduction Technology for Water Resources Management Under Incomplete Information and Dynamic Change

Combining Prediction Models and Dimensionality Reduction Technology for Water Resources Management Under Incomplete Information and Dynamic Change

Mathematical scheduling model of complex industrial process combining swarm intelligence algorithm and swarm dimension reduction technology

The swarm intelligence algorithm is widely used in scheduling problems due to its superior searchability and adaptability, but it faces high computational costs and low efficiency when dealing with high-dimensional complex data. To enhance the handling of large-scale and intricate data, as well as enhance the accuracy and efficiency of scheduling, this study develops a mathematical model for industrial scheduling. The model is based on swarm intelligence algorithms such as symbiotic evolution algorithms, grey wolf algorithms, cuckoo algorithms, population dimensionality reduction techniques, and resource optimal allocation calculation methods. The final test results showed that the accuracy of the model in calculating the objective function was about 80 % in cases of high complexity and about 90 % in cases of low complexity. Compared with other algorithms, its accuracy was improved by 7 %, error rate was reduced by 50 %, time cost was reduced by about 30 %, and conversion rate was fast and more stable on average after 200 generations. The experimental results indicate that the proposed mathematical scheduling model, which integrates multiple intelligent algorithms and population dimensionality reduction technologies, has certain practices and feasibility in large-scale and multi-dimensional production for enterprises. This study combines swarm intelligence algorithms with population dimensionality reduction techniques, which is an innovative attempt at complex industrial scheduling problems.

Detection of Ocean Internal Waves Based on Modified Deep Convolutional Generative Adversarial Network and WaveNet in Moderate Resolution Imaging Spectroradiometer Images

The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset’s size but also exhibit diverse characteristics, enhancing the model’s generalization performance. Furthermore, we have developed a deep neural network named “WaveNet,” which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet’s recognition rate.

Abnormal Status Detection of Catenary Based on TSNE Dimensionality Reduction Method and IGWO-LSSVM Model

Background: Catenary is a crucial component of an electrified railroad's traction power supply system. There is a considerable incidence of abnormal status and failures due to prolonged outside exposure. Driving safety will be directly impacted if an abnormal status or failure occurs. Currently, catenary detection vehicles are the most often utilized technique for gathering data and identifying faults based on manual experience. However, this technology cannot meet the demands of prompt detection and correction of faults in railways engineering due to its extremely low work efficiency. Objective: Based on the above, an abnormal status detection method of catenary based on the improved gray wolf (IGWO) algorithm optimized the least squares support vector machine (LSSVM) with the t-distributed stochastic neighbor embedding (TSNE) is proposed in this paper. In order to improve the accuracy of catenary abnormal status detection and shorten the detection time. Methods: Firstly, the TSNE dimensionality reduction technology is used to reduce the original catenary data to three-dimensional space. Then, in order to address the issue that the parameters of the LSSVM detection model are hard to determine, the improved GWO algorithm is used to optimize the penalty factor and kernel parameter in the LSSVM and establish the TSNE-IGWO-LSSVM catenary abnormal status detection model. Finally, contrasting experimental results of different detection models. The T-distributed Stochastic Domain Embedding (TSNE) is an improved nonlinear dimensionality reduction method based on the Stochastic Neighbor Embedding (SNE). TSNE no longer adopts the distance invariance in linear dimensionality reduction methods such as ISOMAP. TSNE is much better than the linear dimensionality reduction method in the reduction degree of the original dimension. The GWO algorithm, which is frequently used in engineering research, has the advantages of a simple model, great generalization capability, and good optimization performance. The premature convergence is one of the remaining flaws. By applying a good point set to initialize the gray wolf population and the nonlinear control parameters, the gray wolf algorithm is improved in this research. The IGWO algorithm effectively makes up for the problem of balancing the local exploitation and global search capabilities of GWO. Additionally, this IGWO algorithm performs the Cauchy variation operation on the current generation optimal solution to improve population diversity, enlarge the search space, and increase the likelihood of the algorithm escaping the local optimal solution in order to prevent the algorithm from failing the local optimum. The Least Squares Support Vector Machine (LSSVM) is an improved version of the Support Vector Machine (SVM), which replaces the original inequality constraint with a linear least squares criterion for the loss function. The kernel parameters of the RBF function and the penalty factor, these two parameters directly determine the detection effect of LSSVM. In this paper, the IGWO is utilized to adjust and determine the LSSVM parameters in order to enhance the detection capacity of the LSSVM model. Results:: In this paper, in order to minimize the experiment's bias, the training data and the test data are allocated in a ratio of 4:1, the training data are set to 400 groups, and the test data are set to 100 groups. After training the five models, the test data is used to validate and compare the detection capacity of the models. After each of the five detection models was tested ten times, the TSNE-IGWO-LSSVM model is compared with the IGWO-LSSVM model, the TSNE-FA-LSSVM model, the GWO-LSSVM model, and the GWO-ELM model, the results show that the TSNE-IGWO-LSSVM model has the highest average detection accuracy of 97.1% and the shortest running time of 26.9s. For the root mean squared error (RMSE) and the root mean squared error (RMSE), the TSNE-IGWO-LSSVM model is 0.17320 and 2.51% respectively, which is the best among the five models, indicating that it not only has higher detection accuracy but also better convergence of detection accuracy than the other models. Conclusions: With the thousands of miles of catenary and the complexity of the data, it is crucial to shorten the running time in order to improve the efficiency and ease the burden of the processors. The experiments demonstrate that the TSNE-IGWO-LSSVM detection model can detect the abnormal status of catenary more accurately and quickly, providing a new method for the abnormal status detection of catenary, which has certain application value and engineering significance in the era of fully electrified railways.

Comparison of Feature Selection and Feature Extraction Role in Dimensionality Reduction of Big Data

Recently, researchers intensified their efforts on a dataset with a large number of features named Big Data because of the technological revolution and the development in the data science sector. Dimensionality reduction technology has efficient, effective, and influential methods for analyzing this data, which contains many variables. The importance of Dimensionality Reduction technology lies in several fields, including “data processing, patterns recognition, machine learning, and data mining”. This paper compares two essential methods of dimensionality reduction, Feature Extraction and Feature Selection Which Machine Learning models frequently employ. 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. Some techniques are more efficient than others in text data and others are more efficient in dealing with images.

Collective Influence Maximization in Mobile Social Networks

The omnipresence of information cascading process in mobile social networking applications makes the identification of a small set <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> of influential users, which is widely believed to trigger the information outbreak, always an crucial issue in various applications such as the mobile advertising and viral marketing. Formulated as Influence maximization (IM) in 2003, this NP-hard problem has received a multitude of studies with diverse angles. However, these works often unable to provide reliable solutions, due to the loss of an exact metric for evaluating users’ contributions on information cascading in the state-of-the-art sampling based IM schemes. In this paper, we evaluate users in IM based on the collective influence (CI), a metric on the structural features of the users in network graph that reflects the contributions of the users’ neighborhoods on shaping collective dynamics of the users over the whole network. For conducting the influencer identification under probabilistic diffusion model based on the CI, we specify a quantified structural feature of the most influential users from the scope of diffusion over the whole network, and reveal that the structural influence power (CI value) of each user is a weighted cumulation of the diffusion probabilities from neighbors within certain hops. Utilizing CI, we design a novel algorithm which identifies the influencers via iteratively choosing the users with top CI values. Moreover, we point out that directly computing CI values requires to traverse the network which is originally represented by a high-dimensional matrix, and leads to huge complexity of influencer identification. To improve scalability, we further trade precision for efficiency by incorporating network embedding, a dimensionality reduction technology for networks, into algorithm design, and propose a minor variant, where CI is jointly recapitulated by low-dimensional user representations and user degrees. The superiority of our algorithms is empirically validated over 8 datasets, with an increment in influence size up to 50 percent and a comparable or even less running time comparing with existing baselines.

Quantitative evaluation of crack damage under a variable temperature environment based on a Gaussian mixture model

During the operation of high-speed trains, components are prone to fatigue crack damage under the action of cyclic loading and extreme temperature environments. Temperature change has become an important factor for monitoring the health of high-speed train structures. A new fatigue crack size quantification method under a variable temperature environment based on a Gaussian mixture model (GMM) is presented in this paper. A series of damage indexes are proposed to characterize the interaction mechanism between the signal and crack under temperature change. Moreover, multidimensional damage indexes extracted by the fusion of dimensionality reduction technology are used to establish a complete working condition baseline GMM database. The service temperature of the structure is determined according to the maximum similarity criterion between the baseline GMMs and detection GMMs. Furthermore, the quantitative crack length detection model is established at each temperature. To validate the effectiveness of the method, fatigue crack experiments in a variable temperature environment are carried out. The verification results show that this method can detect fatigue crack growth at different temperatures.

PCA-Pruner: Filter pruning by principal component analysis

Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and neural network pruning to alleviate this problem. Among network pruning, filter pruning has received broad attention. At present, most of the filter pruning methods need to define pruning rates manually, which is a trial-and-error process and requires rich experimental experience. Some methods obtain global optimal network parameters by Neural Architecture Search (NAS) or Evolutionary Algorithms (EA) to overcome this difficulty. However, they also introduce huge computational burden. To mitigate the above problems, this study proposes a pruning strategy based on Principal Component Analysis (PCA) called PCA-Pruner. Filter weights of a layer is regarded as a set of features, and the number of filters responding to feature dimension. Then, the number of reserved filters in each layer can be determined by PCA which is a classical dimensionality reduction technology. After that, we calculate the L1 norm of each filter in each layer and use it as an importance measurement to prune filters. Experimental results show that PCA-Pruner achieve performance improvements over the state-of-the-arts algorithms. For example, we compress the FLOPs and parameters of ResNet-56 on CIFA-10 by 45.8% and 47.1%, with an increase in accuracy of 0.27%. For ResNet-110 on CIFAR-10, we improve the accuracy by 0.58% and reduce the FLOPs and Params of the model by 58.3% and 56.2%, respectively. Towards ResNet-56 on CIFAR-100 dataset, we achieve a 38.8% FLOPs decrease and 38.0% Params reduction with only 0.69% accuracy loss.

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020.

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

Clustered Multiple Manifold Metric Learning for Hyperspectral Image Dimensionality Reduction and Classification

Dimensionality reduction (DR) technology is an important part of hyperspectral image (HSI) processing. The DR technology can effectively remove the redundant information in the HSIs and avoid the Hughes phenomenon, which is beneficial to image classification. Metric learning is widely used in DR technology, the goal of which is to achieve DR by maximizing the distance of between-class while minimizing the distance of within-class. However, traditional metric learning does not consider the intrinsic structure of data during the training process, resulting in insufficient information utilization. In order to solve the above problems, this article proposes clustered multiple manifold metric learning (CM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> L) by combining manifold learning with metric learning for DR and classification of HSI. The proposed CM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> L algorithm first divides the original data into multiple independent clusters and regards each sample point and its near point in the cluster as a whole, constructing it as a manifold. Then, CM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> L uses the manifold metric distance to replace the traditional metric distance. Finally, by making full use of the local information of HSI in such a way, the discrimination ability is enhanced. Intensive experimental results on three real HSI datasets validate the effectiveness of our proposed CM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> L algorithm.

Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms

Supervised subspace projection technology is a major method for dimensionality reduction in pattern recognition. At present, most supervised subspace projection algorithms are derived from the multi-dimensional extended version of Fisher linear discriminant analysis (FDA), also known as Multi-dimensional Fisher discriminant analysis (MD-FDA). However, MD-FDA needs to be improved further because the projection vectors in the noise-subspace cannot be sorted and the ill-condition of the within-class scatter matrix may cause severe numerical instabilities. Generalized discriminant component analysis (GDCA), the generalization of MD-FDA, together with its kernelization forms are proposed and correspondingly rigorous mathematical proofs are detailed in this paper. By virtue of 5 validation data sets derived from UCI Machine Learning Repository and our laboratory, the theoretical validity and technical advantages of GDCA as well as its kernelization forms are verified, and the effectiveness of the newly proposed method is demonstrated in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms.

Online early terminated streaming feature selection based on Rough Set theory

Feature selection is a vital dimensionality reduction technology for machine learning and data mining that aims to select a minimal subset from the original feature space. Traditional feature selection methods assume that all features can be required before learning, while features may exist in a stream mode for some real-world applications. Therefore, online streaming feature selection was proposed to handle streaming features on the fly. When the feature dimension is extraordinarily high or even infinite, it is time-consuming or impractical to wait for all the streaming features to arrive. Motivated by this, we study and solve the exciting issue of whether we can terminate the online streaming feature selection early for efficiency while maintaining satisfactory performance for the first time. Specifically, we first formally define the problem of online early terminated streaming feature selection and summary two properties that the early terminated mapping function should satisfy. Then we choose the dependency degree function in Rough Set theory as our early terminated mapping function and demonstrate that it satisfies the two properties. Based on this, we propose a novel Early Terminated Online Streaming Feature Selection framework, named OSFS-ET, which could terminate the streaming feature selection early before the end of streaming features and guarantee a competing performance with the currently selected features. Extensive experiments on twelve real-world datasets demonstrate that OSFS-ET can be far faster than state-of-the-art streaming feature selection methods while maintaining excellent performance on predictive accuracy.

Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease.

MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.

Compact finite-difference method for 2D time-fractional convection–diffusion equation of groundwater pollution problems

In this work, we provide a compact finite-difference scheme (CFDS) of 2D time-fractional convection–diffusion equation (TF-CDE) for solving fluid dynamics problem, especially groundwater pollution. The successful predication of the pollutants concentration in groundwater will greatly benefit the protection of water resources for provide the fast and intuitive decision-makings in response to sudden water pollution events. Here, we creatively use the dimensionality reduction technology (DRT) to rewrite the original 2D problem as two equations, and we handle each one as a 1D problem. Particularly, the spatial derivative is approximated by fourth-order compact finite-difference method (CFDM) and time-fractional derivative is approximated by $$L_{1}$$ interpolation of Caputo fractional derivative. Based on the approximations, we obtain the CFDS with fourth-order in spatial and $$(2-\alpha )$$ -order in temporal by adding two 1D results. In addition, the unique solvability, unconditional stability, and convergence order $${\mathscr {O}}(\tau ^{2-\alpha } + h_{1}^4 + h_{2}^4)$$ of the proposed scheme are studied. Finally, several numerical examples are carried out to support the theoretical results and demonstrate the effectiveness of the CFDS based DRT strategy. Obviously, the method developed in 2D TF-CDE of groundwater pollution problem can be easily extended for the other complex problems.

Short Text Embedding Autoencoders With Attention-Based Neighborhood Preservation

Shortness and sparsity often plague short text representation for clustering and classification. A popular solution is to extract meaningful low-dimensional embeddings as short text representation via various Dimensionality Reduction technology. However, the existing methods, such as topic models and neural networks, discover low-dimensional embeddings from the whole training sets without considering the geometrical information of short text manifold, resulting in an inability to provide a discriminative embedding of short text. In this paper, we propose a manifold-regularized method, namely Short Texts Embedding AutoEncoders (STE-AEs), aiming to incorporate the semantics from the neighborhood into a regularization training of AutoEncoders (AEs) to extract discriminative low-dimensional short text embeddings. STE-AEs first determines semantics neighborhood via an attention-based weighted matching distance and then preserves the local geometrical structure by incorporating a minimization of the weighted cross-entropy of nearby texts’ embeddings into a regularization training of AEs. Finally, the encoder can act as a parametrized mapping function between observations and embeddings. Furthermore, based on the activation values of the encoder for the training set, STE-AEs employs a regression model of Random Forest (RF) to determine the feature importance so as to find certain informative and readable words for embeddings interpretation. Through extensive experiments on three real-world short text corpuses, the evidence demonstrate that STE-AEs can capture the intrinsic discriminative explanatory factors, improving the performance of short text clustering and classification. Moreover, some understandable words can be efficiently discovered to promote the interpretability of low-dimensional embeddings.

Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image

Dimensionality reduction (DR) technology plays an important role in hyperspectral image (HSI) classification. However, many existing DR algorithms ignore the complex intrinsic structure in spatial domain and spectral domain of HSI. To address this issue, we put forward a spatial-spectral local discriminant projection (SSLDP) method based on the manifold learning theory and spatial consistency in HSI. In SSLDP, hyperspectral pixels are reconstructed by minimizing the weighted reconstruction errors to preserve the local geometric structure. Then, two weighted scatter matrices are designed to maintain the neighborhood structure in spatial domain and two reconstruction graphs are constructed to discover the local discriminant relationship in spectral domain. Finally, an objective function is designed for obtaining an optimal projection by compacting the spatial-spectral local intraclass points while separating the spatial-spectral local interclass points. The experiments performed on some real hyperspectral images, including the Indian Pines, PaviaU and Washington DC, demonstrate that the presented SSLDP algorithm is significantly superior to some state-of-the-art DR algorithms.

Method of Analyzing Transformer DC Magnetic Bias Based on Big Data Cleaning and Dimensionality Reduction

Transformer DC magnetic bias is difficult to accurately analyze and predict because it is caused by complex factors and has a wide influence. This paper combines big data with DC bias magnetic analysis method, making a breakthrough in the DC bias prediction model that only considers a single factor. From the perspective of multi-system coupling, this paper analyzes the modeling of DC magnetic bias based on multi-dimensional big data, and extracts the key influencing factors of transformer DC bias through multi-system big data cleaning and dimensionality reduction technology, thus providing an effective basis for “one-button sequence control” bias magnetic treatment.

Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification

Hyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies plentiful information to distinguish various land-cover types. However, these spectral bands ordinarily contain a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band/feature selection is indispensable for HSI classification. Based on improved subspace decomposition (ISD) and the artificial bee colony (ABC) algorithm, this paper proposes a band selection technique known as ISD–ABC to address the problem of dimensionality reduction in HSI classification. Subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands and using the visualization result of the HSI spectral curve. The artificial bee colony algorithm is first applied to optimize the combination of selected bands with the guidance of ISD and maximum entropy (ME). Using the selected band subset, support vector machine (SVM) with five-fold cross validation is applied for HSI classification. To evaluate the effectiveness of the proposed method, experiments are conducted on two AVIRIS datasets (Indian Pines and Salinas) and a ROSIS dataset (Pavia University). Three indices, namely, overall accuracy (OA), average accuracy (AA) and kappa coefficient (KC), are used to assess the classification results. The experimental results successfully demonstrate that the proposed method provides good classification accuracy compared with six other state-of-the-art band selection techniques.

QRKISS: A Two-Stage Metric Learning via QR-Decomposition and KISS for Person Re-Identification

Person re-identification is a challenging task in the field of intelligent video surveillance because there are wide variations between pedestrian images. As a classical metric learning method, Keep It Simple and Straightforward (KISS) has shown good performance for person re-identification. However, when the dimension of data is high, the KISS method may perform poorly because of small sample size problem. A common solution to this problem is to apply dimensionality reduction technologies to original data before the KISS metric learning, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In this paper, to learn a discriminant and robust metric, we propose a novel two-stage metric learning via QR-Decomposition and KISS, named QRKISS. The first stage of QRKISS is to project original data into a lower dimensional space by QR decomposition. In this lower dimensional space, the trace of the covariance matrix of interpersonal differences can reach maximum. Based on KISS method, the second stage of QRKISS obtains a Mahalanobis matrix in the low-dimension space. We conduct thorough validation experiments on the VIPeR, PRID 450S and CUHK01 datasets, which demonstrate that QRKISS method is better than other KISS-based metric learning methods and achieves state-of-the-art performance.