Soft sensor of melt index based on sparse least square support vector machine

Twin support vector machine (TSVM) has been widely applied to classification problems. But TSVM is sensitive to outliers and is not efficient enough to realize feature selection. To overcome the shortcomings of TSVM, we propose a novel sparse twin support vector machine with the correntropy-induced loss (C-STSVM), which is inspired by the robustness of the correntropy-induced loss and the sparsity of the \(\ell _1\)-norm regularization. The objective function of C-STSVM includes the correntropy-induced loss that replaces the hinge loss, and the \(\ell _1\)-norm regularization that can make the decision model sparse to realize feature selection. Experiments on real-world datasets with label noise and noise features demonstrate the effectiveness of C-STSVM in classification accuracy and confirm the above conclusion further.

Sparse pinball twin support vector machines

Due to the limited resolution of hyperspectral sensors, anomalous targets expressed with subpixels are often mixed with nonhomogeneous backgrounds. This fact makes anomalies difficult to be distinguished from the surrounding background. From this perspective, we propose a novel hyperspectral anomaly detection (AD) algorithm based on low-rank & sparse matrix decomposition (LRaSMD) and support vector machine (SVM). First, based on the LRaSMD technique, the Go decomposition (GoDec) model is utilized to decompose the original image into three components: background, anomalies and noise. In this way, the robust background can be obtained. Subsequently, a clustering algorithm is employed to pick some obvious anomalies. Accordingly, we use both samples of background and anomaly to train an SVM model. The original dataset is sent into the SVM model and both anomalous components and background components can be classified. Experiments on a synthetic hyperspectral image validate the performance of the proposed method.

Scene classification based on single-layer SAE and SVM

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

Most industrial gases such as methane(CH₄), ethylene (C₂H₄) and sulfur hexafluoride (SF6) have obvious absorption characteristics in the infrared band. The infrared absorption spectrum of leaking gas can be obtained through multispectral or hyper-spectral detection technologies to realize gas identification. However, these methods need a lot of work calibrating the detector response curve to target gas. In this work, a sparse infrared absorption spectrum based support vector machine (SVM) recognition method is proposed to obtain the gas absorption peak information without response curve calibration. An uncooled infrared imaging component is utilized to compose a multi-broadband long-pass differential filter infrared imaging setup that filters in the range of 7.5μm ~13.5 μm. Data extracted from multi-band infrared images of C2H4 and SF6 collected by the setup, combined with the simulated data generated by the simulated sparse spectrum algorithm, constitute training set to SVM. C₂H₄ and SF₆ can be accurately identified under laboratory conditions with the path-concentration of 500 ppm·m ~1000 ppm·m. The easy to implement and cost-effective method is expected to realize real-time identification of leaking gas.

Effective common spatial pattern (CSP) feature extraction for motor-imagery (MI) EEG recordings usually depends on the filter band selection to a large extent. However, the most proper filter band can hardly be determined manually due to its subject-specific property. This study introduces a sparse support vector machine (SSVM) approach to implement simultaneous CSP feature selection and classification for MI-based brain–computer interface (BCI). In SSVM, CSP features are first extracted on multiple signals that are filtered from raw EEG data at a set of overlapping subbands. SVM with l 1-norm regularization is then proposed to classify MI tasks with automatic selection of filter bands giving the significant CSP features. The effectiveness of SSVM for MI classification is demonstrated on the BCI Competition III dataset IVa, in comparison with several other competing methods. Experimental results indicate that the proposed SSVM method is promising for development of an improved MI-based BCI.

Sparse elastic net multi-label rank support vector machine with pinball loss and its applications

Sparse discriminant twin support vector machine for binary classification

This paper addresses the problem of feature selection for the classification of ground penetrating radar signals. We propose a new classification approach based on time-frequency analysis and sparse kernel feature selection. In the proposed approach, a time-frequency or a time-scale transform is first applied to the one-dimensional radar trace. Sparse kernel feature selection is then employed to extract an optimum set of features for classification. The sparse kernel method is formulated as an underdetermined linear system in a high-dimensional space, and the category labels of the training samples are used as measurements to select the most informative features. The proposed approach is evaluated through an industrial application of assessing railway ballast fouling conditions. Experimental results show that the proposed combination of sparse kernel feature selection and support vector machine classification yields very high classification rates using only a small number of features.

15 - Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Capturing CO2 from the flue gas of a power plant based on chemical absorption method is an effective way to cut down the emission of CO2. However, the principal components in the solvent are very difficult to be measured online. Therefore an online measurement and control system was designed for the process of capturing CO2 by MEA chemical absorption. The feature selection was done according to Bayesian networks. Furthermore based on the principle of a new sparse support vector machine (SLS-SVM), the measurement of ionic species distribution in the absorption solution was predicted, and the results agreed well with the data obtained by Nuclear Magnetic Resonance (NMR) measurement. The mean square error is 3.8645×10-7. The measurement of solution species distribution was carried out online by using infrared sensors and NMR technology. Meanwhile the sampling system and NMR unit were linked for the online analysis based on variable-temperature and variable-pressure NMR technology. Finally a measurement and control platform was constructed based on VI technology. It shows that the performance of traditional equipment can be improved by the novel system, which will provide a more effective method for CO2 capture process.

New algorithms for the numerical solution of optimization problems involving the $$l_{0}$$ pseudo-norm are proposed. They are designed to use a recently proposed computational methodology that is able to deal numerically with finite, infinite and infinitesimal numbers. This new methodology introduces an infinite unit of measure expressed by the numeral $$\textcircled {1}$$ (grossone) and indicating the number of elements of the set $${\text {I}}\!{\text {N}}$$ , of natural numbers. We show how the numerical system built upon $$\textcircled {1}$$ and the proposed approximation of the $$l_0$$ pseudo-norm in terms of $$\textcircled {1}$$ can be successfully used in the solution of elastic net regularization problems and sparse support vector machines classification problems.

Soft Sensor for Melt Index Prediction Based on Long Short-Term Memory Network

We introduce a family of Newton-type greedy selection methods for -constrained minimization problems. The basic idea is to construct a quadratic function to approximate the original objective function around the current iterate and solve the constructed quadratic program over the cardinality constraint. The next iterate is then estimated via a line search operation between the current iterate and the solution of the sparse quadratic program. This iterative procedure can be interpreted as an extension of the constrained Newton methods from convex minimization to non-convex -constrained minimization. We show that the proposed algorithms converge asymptotically and the rate of local convergence is superlinear up to certain estimation error. Our methods compare favorably against several state-of-the-art greedy selection methods when applied to sparse logistic regression and sparse support vector machines.