Laplacian Support Vector Machine Research Articles

Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning

In recent years, multi-view semi-supervised learning has gradually become a popular research direction. The classic binary classification methods in this field are multi-view Laplacian support vector machines (MvLapSVM) and multi-view Laplacian twin support vector machines (MvLapTSVM), which extend semi- supervised support vector machine to multi-view learning. Nevertheless, similar to the majority of SVM-based multi-view methods, the above methods are two-view methods that cannot fully leverage the information from all views and are constructed based on the L2 norm. Additionally, in semi-supervised graph learning, the quality of the graph often has a significant impact on the results. Therefore, we propose a novel multi-view hypergraph regularized Lp norm least squares twin support vector machines (MvHGLpLSTSVM) that can handle general multi-view data for semi-supervised learning. It extends hypergraph learning to multi-view learning and combines Lp norm to further explore the manifold structure and embedded geometric information of multi-view data. By using equality constraints, we design a simple and effective iterative algorithm. In the classification of six multi-view datasets, we compare the proposed method with some other state-of-the-art methods, and the results show that the proposed method is effective.

Machine learning classification approaches to optimize ROP and TOB using drilling and geomechanical parameters in a carbonate reservoir

Drilling optimization has been broadly developed in terms of influential parameters. The assessment time and the effects of both geomechanical and drilling parameters were vital challenges of investigations. Drilling factors are applied force or rotation of drilling agents such as weight on bit (WOB), and geomechanical features represent mechanical indexes of rocks including unconfined compressive strength (UCS). Optimization efforts have been demonstrated on complex prediction methods whereas the simplicity of classification can offer some optimal ranges utilizing machine learning classifications in an accelerated process. In this study, a novel procedure using the supervised and semi-supervised learning approaches was conducted to classify and optimize the rate of penetration (ROP) and torque on bit (TOB). Firstly, in the case well, user-defined classes were assigned based on geomechanical units (GMU) and the ranges of high ROP and low TOB, thus classes divided drilling factors as GMUs of the case. Secondly, the feature selection was carried out by neural pattern recognition with three multi-objective optimization methods for classification. The inputs of classifications were WOB, hook load, pump pressure, flow rate, UCS, and internal friction angle. Classification approaches were decision trees, support vector machine (SVM), and ensemble learning. Finally, the bagged trees permutation and Laplacian SVM (LapSVM) algorithm separately revealed the significance of parameters and predicted the optimal ROP and TOB regions. Findings showed (1) in supervised classification of the case well, the cubic SVM and bagged trees had the highest area under the curve (AUC) and accuracy, on average 0.97 and 0.96, respectively. (2) The average accuracy of the supervised classifications in a test well was 91% except for the fine SVM, which makes them reliable for the fields with the least information. (3) The permutation outcomes for significant features, flow rate and UCS, exposed influential parameters for ROP and TOB optimization. (4) The semi-supervised method, LapSVM, not only acquired both ROP and TOB labels with an accuracy of 88% but also presented their optimal ranges in 95% of the assessed zones. (5) LapSVM deals with a limited training section perfectly opposed to the supervised version, which is vital for drilling investigation. (6) Implementing machine learning classification approaches with rock properties is a key factor in achieving effective drilling parameters in less time. More importantly, the recommended drilling factors concerning geomechanical properties can ameliorate both drilling performance and perception of upcoming collapse.

Laplacian Support Vector Machines as Data Classifier in Machine Learning Approaches of Structural Health Monitoring

Laplacian Support Vector Machines as Data Classifier in Machine Learning Approaches of Structural Health Monitoring

RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINE

Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.

Lightning risk assessment of offshore wind farms by semi-supervised learning

The wind turbine has rapidly developed worldwide with increasing height and scale, resulting in the increased risk of lightning strikes. When wind turbines were stroke by the lightning, they will be damaged, causing economic loss and outage. Lightning risk assessment can guide the improvement of lightning protection and the design of the wind farms to efficiently prevent lightning damages. The traditional lightning risk assessment methods rely on subjective features to some extent. The existing lightning risk assessment methods based on machine learning demand abundant labeled data. It is extremely difficult to label and acquire the data. This paper proposed a lightning risk assessment method based on semi-supervised learning to address the challenges of labeling negative samples and limited labeled data. The semi-supervised K-means algorithm is proposed to divide all data into three parts. The Laplacian support vector machine (LapSVM) with hyperparameters optimized by the particle swarm optimization (PSO) is used to assess the lightning risk. The proposed method has better performance than the standard SVM and neural network (NN). Moreover, previous researches did not consider lightning protection ability. This paper introduces the receptor number into lightning risk assessment. The assessment results suggest that there is higher lightning risk in areas with a great number of wind turbines, high lightning density, and strong lightning strength. The ocean area is more likely to have low lightning risk. The results are valuable for lightning protection optimization of existing wind farms and can give guidance for the plan of new wind farms.

Multiclass Laplacian support vector machine with functional analysis of variance decomposition

In classification problems, acquiring a sufficient amount of labeled samples sometimes proves expensive and time-consuming, while unlabeled samples are relatively easier to obtain. The Laplacian Support Vector Machine (LapSVM) is one of the successful methods that learn better classification functions by incorporating unlabeled samples. However, since LapSVM was originally designed for binary classification, it can not be applied directly to multiclass classification problems commonly encountered in practice. Thus we derive an extension of LapSVM to multiclass classification problems using an appropriate multiclass formulation. Another problem with LapSVM is that irrelevant variables easily degrade classification performance. The irrelevant variables can increase the variance of predicted values and make the model difficult to interpret. Therefore, this paper also proposes the multiclass LapSVM with functional analysis of variance decomposition to identify relevant variables. Through comprehensive simulations and real-world datasets, we demonstrate the efficiency and improved classification performance of the proposed methods.

Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification

Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM.

Identification of coal structures by semi-supervised learning based on limited labeled logging data

Coal structure is a critical parameter in coalbed methane (CBM) development due to its significant impacts on methane enrichment, fluid flow and hydraulic fracturing. Traditional statistical analysis and data-driven machine learning methods for coal structure identification are highly dependent on the labeled logging data and have potential limitations when labeled logging data is limited. To address this issue, this paper proposed a semi-supervised learning method based on Laplacian support vector machine (LapSVM) to identify coal structure by using few labeled logging data. By mining the structure information from abundant unlabeled data, LapSVM can improve the model performance and alleviate the over-reliance on labeled data. To evaluate and verify the effectiveness and reliability of the proposed LapSVM method in coal structure identification, datasets collected from 32 CBM wells in the southern Qinshui Basin, China, are utilized in this study. The particle swarm optimization (PSO) is adopted for parameter optimization of LapSVM models. For the LapSVM model, the addition of unlabeled data is conducive to enhance model accuracy, and unavoidably increases the computational cost at the same time. The comparison of training, testing and blind-well test results between the LapSVM and standard support vector machine (SVM) models indicates that the LapSVM outperforms traditional SVM and possesses higher accuracy and generalization in coal structure identification. It has been demonstrated that the LapSVM can be a reliable tool for coal structure identification when limited labeled logging data is available.

Correction to: Adaptive Laplacian Support Vector Machine for Semi-supervised Learning

Correction to: Adaptive Laplacian Support Vector Machine for Semi-supervised Learning

A Semisupervised Learning Framework for Recognition and Classification of Defects in Transient Thermography Detection

The defect classification task is of great benefit to evaluating the safety performance of equipment and providing useful feedback information for discovering production process problems. In this article, we present a semisupervised learning (SSL) framework for transient thermography detection to employ the temporal and spatial information encoded into the three-dimensional transient thermal tensor data and provide pixel-level classification results for defect types. The time- and frequency-domain physical models for the transient thermal evolution of different kinds of defects are established to illustrate the theoretical foundation of defects classification based on transient thermography. The semisupervised multiclass Laplacian support vector machine is proposed to enable involving the abundant unlabeled data for enhancing learning performance in practical industrial applications where labeled samples are insufficient and labeling work is costly and laborious. A case study on silicone insulating materials with various types of artificial simulated internal defects validates the stronger generalized ability of the proposed method. This work, for the first time, proposes an SSL framework in transient thermography-based defect detection studies. It is believed that our proposed method is quite inspired for introducing SSL techniques to transient thermography for preferable performance in practical industrial applications.

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

Background: The identification of DNA binding proteins (DBP) is an important research field. Experiment-based methods are time-consuming and labor-intensive for detecting DBP. Objective: To solve the problem of large-scale DBP identification, some machine learning methods are proposed. However, these methods have insufficient predictive accuracy. Our aim is to develop a sequence- based machine learning model to predict DBP. Methods: In our study, we extracted six types of features (including NMBAC, GE, MCD, PSSM-AB, PSSM-DWT, and PsePSSM) from protein sequences. We used Multiple Kernel Learning based on Hilbert- Schmidt Independence Criterion (MKL-HSIC) to estimate the optimal kernel. Then, we constructed a hypergraph model to describe the relationship between labeled and unlabeled samples. Finally, Laplacian Support Vector Machines (LapSVM) is employed to train the predictive model. Our method is tested on PDB186, PDB1075, PDB2272 and PDB14189 data sets. Result: Compared with other methods, our model achieved best results on benchmark data sets. Conclusion: The accuracy of 87.1% and 74.2% are achieved on PDB186 (Independent test of PDB1075) and PDB2272 (Independent test of PDB14189), respectively.

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

Identification of DNA-Binding Proteins via Hypergraph Based Laplacian Support Vector Machine

A Semi‐supervised Classification Method of Parasites Using Contrastive Learning

An expected shortfall of supervised learning for medical imaging classification is the insatiable need for human annotation, which is very expensive. Further, for microscopic parasite images, the salient structures are generally fuzzy, and the insignificant textures are noising. This paper proposes a semi‐supervised classification method of three parasites and Erythrocytes microscopic images using a large amount of unlabeled data and a small amount of labeled data for training. It contains a feature extractor trained by contrastive learning and a classifier optimized by Laplacian Support Vector Machine (LapSVM). First, for the deep Convolutional Neural Network feature extractor, we introduce real‐world images with similar and clear semantic information to enhance the structure at the representation level. In addition, we introduce variant appearance transformations to eliminate the texture at the representation level. Second, the gated linear network is adopted as the classifier to realize a piecewise linear separation boundary. The parameters are optimized implicitly by LapSVM using a kernel function composed of the representation and the gate control signals, which are generated from the learned feature extractor. The proposed method shows excellent performance when only 1% of the microscopic images are labeled. As a result, we achieve affordable and accurate diagnostic testing in parasite classification. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

Adaptive Laplacian Support Vector Machine for Semi-supervised Learning

AbstractLaplacian support vector machine (LapSVM) is an extremely popular classification method and relies on a small number of labels and a Laplacian regularization to complete the training of the support vector machine (SVM). However, the training of SVM model and Laplacian matrix construction are usually two independent process. Therefore, In this paper, we propose a new adaptive LapSVM method to realize semi-supervised learning with a primal solution. Specifically, the hinge loss of unlabelled data is considered to maximize the distance between unlabelled samples from different classes and the process of dealing with labelled data are similar to other LapSVM methods. Besides, the proposed method embeds the Laplacian matrix acquisition into the SVM training process to improve the effectiveness of Laplacian matrix and the accuracy of new SVM model. Moreover, a novel optimization algorithm considering primal solver is proposed to our adaptive LapSVM model. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics on both real datasets and synthetic datasets.

A study on intelligent diagnosis model of shortwave receiving system based on improved KFCM and LapSVM

Aiming at the difficulty of obtaining a large number of labeled samples of the shortwave receiving system, an intelligent diagnosis method for the shortwave receiving system based on the improved Laplacian SVM algorithm is proposed. By introducing the idea of neighborhood density into the adjacency graph construction of Laplacian SVM, the manifold structure information of samples is more fully mined, thus improving the performance of Laplacian SVM classifier and realizing the optimization of traditional Laplacian SVM. KFCM clustering algorithm was used to select unlabeled boundary samples and labeled samples to form the reduction training set. The method of the KFCM pre-selection sample was combined with the improved Laplacian SVM algorithm to enhance the learning efficiency. The simulation results using the UCI data set and the experimental verification results of shortwave receiving system sample data indicate that the proposed algorithm could more fully mine the manifold structure information of samples and improve the performance of the Laplacian SVM classifier.

A Laplacian SVM Based Semi‐Supervised Classification Using Multi‐Local Linear Model

Semi‐supervised learning considers a classification problem of learning from both labeled and unlabeled data. This paper proposes a semi‐supervised classification method, in which the potential separation boundary is detected and its information is ingeniously incorporated into a Laplacian support vector machine (LapSVM) in both kernel level and graph level. By applying a pseudo‐labeling approach, the input space is first divided into several linear separable partitions along the potential separation boundary. A multi‐local linear model is then built for the separation boundary, by interpolating multiple local linear models assigned to the local linear separable partitions. The multi‐local linear model is further formulated into a linear regression form with a new input vector in the spanned feature space, which contains the information of potential separation boundary. Then the linear parameters are estimated globally by a LapSVM algorithm. Furthermore, the input in the spanned feature space and pseudo labels are used to construct a label guided graph. Numerical experiments on various real‐world datasets and visual representation on toy example exhibit the effectiveness of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

L1-norm Laplacian support vector machine for data reduction in semi-supervised learning

As a semi-supervised learning method, Laplacian support vector machine (LapSVM) is popular. Unfortunately, the model generated by LapSVM has a poor sparsity. A sparse decision model has always been fascinating because it could implement data reduction and improve performance. To generate a sparse model of LapSVM, we propose an $$\ell _1$$ -norm Laplacian support vector machine ( $$\ell _1$$ -norm LapSVM), which replaces the $$\ell _2$$ -norm with the $$\ell _1$$ -norm in LapSVM. The $$\ell _1$$ -norm LapSVM has two techniques that can induce sparsity: the $$\ell _1$$ -norm regularization and the hinge loss function. We discuss two situations for the $$\ell _1$$ -norm LapSVM, linear and nonlinear ones. In the linear $$\ell _1$$ -norm LapSVM, the sparse decision model implies that features with nonzero coefficients are contributive. In other words, the linear $$\ell _1$$ -norm LapSVM can perform feature selection to achieve the goal of data reduction. Moreover, the nonlinear (kernel) $$\ell _1$$ -norm LapSVM can also implement data reduction in terms of sample selection. In addition, the optimization problem of the $$\ell _1$$ -norm LapSVM is a convex quadratic programming one. That is, the $$\ell _1$$ -norm LapSVM has a unique and global solution. Experimental results on semi-supervised classification tasks have shown a comparable performance of our $$\ell _1$$ -norm LapSVM.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

Identification of DNA-Binding Proteins via Hypergraph Based LaplacianSupport Vector Machine

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