Multiple Kernel Research Articles

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

Automated Buffer Sizing of Dataflow Applications in a High-level Synthesis Workflow

High-Level Synthesis (HLS) tools are mature enough to provide efficient code generation for computation kernels on FPGA hardware. For more complex applications, multiple kernels may be connected by a dataflow graph. Although some tools, such as Xilinx Vitis HLS, support dataflow directives, they lack efficient analysis methods to compute the buffer sizes between kernels in a dataflow graph. This article proposes an original method to safely approximate such buffer sizes. The first contribution computes an initial overestimation of buffer sizes without knowing the memory access patterns of kernels. The second contribution iteratively refines those buffer sizes, thanks to cosimulation. Moreover, the article introduces an open source framework using these methods to facilitate dataflow programming on FPGA using HLS. The proposed methods and framework have been tested on seven dataflow applications and outperform Vitis HLS cosimulation in five benchmarks, either in terms of BRAM and LUT usage, or in terms of exploration time. In the two other benchmarks, our best method gets results similar to Vitis HLS. Last but not least, our method admits directed cycles in the application graphs.

WMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

A stacked multiple kernel support vector machine for blast induced flyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and the induced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one of the most important issues induced by blasting operations, since the accurate prediction of which is crucial for delineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets of blasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stacked MK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve the model performance by addressing the importance level of different features. For comparison purpose, 6 other machine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), Artificial Neural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validation process for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVM model achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95 and 99.25 in training and testing phase, respectively.

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

MSV-net: Multi-scale visual-inspired network for short-term electricity price forecasting

In electricity price forecast, deep networks have shown promising due to their strong nonlinear modeling capability but corresponding design tricks are not convincible, reflecting the situation that biologically-inspired networks in time-series analysis are still not enough explored. To this end, this paper proposes a multi-scale visual-inspired network with three explored modules, namely MSV-Net, for electricity price forecasting. In particular, through simulating both the ocular globe and neurons in signal conversion and further enhancement, the multi-scale feature extraction module captures the overall and specific time-scale features via multiple convolutional kernels and especially highlights the core features via self-attention mechanisms. Through imitating the left and right hemispheres in analyzing and producing complementary visual information, the dual-stream collaboration module adopts the convolutional layer and gated recurrent unit to extract and integrate time-dependent relationships in parallel, thus improving the model stability. Through simulating the higher visual region in understanding complex environments, the forecasting module is introduced to forecast the electricity price. The layer-by-layer collaboration of the above three modules guarantees the effectiveness of the MSV-Net. Moreover, four comprehensive evaluation metrics are explored to assess the forecasting performance in peak, low, and normal samples. Experiments and discussions under two electricity price datasets show that the MSV-Net has satisfactory forecasting accuracy and stability, in improving average mean absolute percent error of 31.59 % and 25.15 % for two datasets compared with 12 baselines, and shows extensible in other energy forecasting fields.

Attention-based fusion of multiple graphheat networks for structural to functional brain mapping

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (Desikan-Killiany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of the structure-function relation of the human brain.

Multiple Kernel Synthesis of Head CT Using a Task-Based Loss Function

In CT imaging of the head, multiple image series are routinely reconstructed with different kernels and slice thicknesses. Reviewing the redundant information is an inefficient process for radiologists. We address this issue with a convolutional neural network (CNN)-based technique, synthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON), that creates a single, thin, low-noise series that combines the favorable features from smooth and sharp head kernels. ZIRCON uses a CNN model with an autoencoder U-Net architecture that accepts two input channels (smooth- and sharp-kernel CT images) and combines their salient features to produce a single CT image. Image quality requirements are built into a task-based loss function with a smooth and sharp loss terms specific to anatomical regions. The model is trained using supervised learning with paired routine-dose clinical non-contrast head CT images as training targets and simulated low-dose (25%) images as training inputs. One hundred unique de-identified clinical exams were used for training, ten for validation, and ten for testing. Visual comparisons and contrast measurements of ZIRCON revealed that thinner slices and the smooth-kernel loss function improved gray-white matter contrast. Combined with lower noise, this increased visibility of small soft-tissue features that would be otherwise impaired by partial volume averaging or noise. Line profile analysis showed that ZIRCON images largely retained sharpness compared to the sharp-kernel input images. ZIRCON combined desirable image quality properties of both smooth and sharp input kernels into a single, thin, low-noise series suitable for both brain and skull imaging.

Few-shot segmentation using distribution adaption networks

Few-shot segmentation aims to achieve object-specific semantic segmentation in query images by conditioning on specific objects identified within support images. Previous methodologies predominantly relied on evaluating similarities between semantic-level prototypes of support features or comprehensive pixelwise support information and query images to inform predictions for queries. However, a significant domain discrepancy has been identified between the extracted query features and their support counterparts, resulting in a significant misalignment in the distribution of support and query features. As a result, direct analogies between support and query information cannot ensure the precision of query predictions. In this investigation, a distribution adaptation network is introduced, meticulously designed to learn discriminative representations and alleviate the distributional disparities between support features and query features. The adaptation network embeds both support and query features into Reproducing Kernel Hilbert Spaces (RKHS) and optimally aligns their distributions. The distributional gap is effectively bridged through optimal kernel embedding alignment, facilitated by a multiple kernel learning strategy. Extensive experimentation on datasets PASCAL-5i, COCO-20i and FSS-1000 unequivocally demonstrates that the proposed Kernel-Aligned Distribution Network (KADN) achieves state-of-the-art performance.

Solve Riemann–Liouville boundary value problems using collocation boundary value methods with the graded mesh

The paper explores numerical approaches to a class of fractional boundary value problems that can be converted into Volterra integral equations with multiple singular kernels. We propose a collocation boundary value method with a graded mesh for the resulting weakly singular integral equations. Moreover, we demonstrate the local convergence property of the presented method using the Gronwall’s inequality. In addition, we assess the stability of the algorithm by examining the L2-norm of the approximate solution. It is found that the proposed collocation method exhibits both fast convergence order and good stability. The numerical experiments provide rigorous validation of the theoretical results.

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.

Multiple Riemannian Kernel Hashing for Large-Scale Image Set Classification and Retrieval.

Conventional image set methods typically learn from small to medium-sized image set datasets. However, when applied to large-scale image set applications such as classification and retrieval, they face two primary challenges: 1) effectively modeling complex image sets; and 2) efficiently performing tasks. To address the above issues, we propose a novel Multiple Riemannian Kernel Hashing (MRKH) method that leverages the powerful capabilities of Riemannian manifold and Hashing on effective and efficient image set representation. MRKH considers multiple heterogeneous Riemannian manifolds to represent each image set. It introduces a multiple kernel learning framework designed to effectively combine statistics from multiple manifolds, and constructs kernels by selecting a small set of anchor points, enabling efficient scalability for large-scale applications. In addition, MRKH further exploits inter- and intra-modal semantic structure to enhance discrimination. Instead of employing continuous feature to represent each image set, MRKH suggests learning hash code for each image set, thereby achieving efficient computation and storage. We present an iterative algorithm with theoretical convergence guarantee to optimize MRKH, and the computational complexity is linear with the size of dataset. Extensive experiments on five image set benchmark datasets including three large-scale ones demonstrate the proposed method outperforms state-of-the-arts in accuracy and efficiency particularly in large-scale image set classification and retrieval.

Learning With Multiple Kernels

Learning With Multiple Kernels

Performance Evaluation of Support Vector Regression Machine Models in Water Level Forecasting

Understanding and predicting water level rise is crucial for effective water resource management, flood control, disaster risk reduction, and adaptation to climate change. This study utilize the use of Support Vector Regression Machine (SVRM) models in predicting the water level of Mandulog River in the Philippines. To evaluate its predictive capability, two data architectures, various kernels, and parameters were examined to find the optimal configuration. Multiple kernels were compared for accurate pattern discovery, and parameter selection was performed to enhance predictive accuracy. The SVRM models achieved the lowest Mean Average Percentage Error (MAPE) of 2.186%, Root-Mean Squared Error (RMSE) of 0.00601 during the dry season, and MAPE of 2.226% and RMSE of 0.00602 during the rainy season. Both models were deemed suitable for watershed level forecasting.

Regularized Simple Multiple Kernel k -Means With Kernel Average Alignment.

Multiple kernel clustering (MKC) aims to learn an optimal kernel to better serve for clustering from several precomputed basic kernels. Most MKC algorithms adhere to a common assumption that an optimal kernel is linearly combined by basic kernels. Based on a min-max framework, a newly proposed MKC method termed simple multiple kernel k -means (SimpleMKKM) can acquire a high-quality unified kernel. Although SimpleMKKM has achieved promising clustering performance, we observe that it cannot benefit from any prior knowledge. This would cause the learned partition matrix may seriously deviate from the expected one, especially in clustering tasks where the ground truth is absent during the learning course. To tackle this issue, we propose a novel algorithm termed regularized simple multiple kernel k -means with kernel average alignment (R-SMKKM-KAA). According to the experimental results of existing MKC algorithms, the average partition is a strong baseline to reflect true clustering. To gain knowledge from the average partition, we add the average alignment as a regularization term to prevent the learned unified partition from being far from the average partition. After that, we have designed an efficient solving algorithm to optimize the new resulting problem. In this way, both the incorporated prior knowledge and the combination of basic kernels are helpful to learn better unified partition. Consequently, the clustering performance can be significantly improved. Extensive experiments on nine common datasets have sufficiently demonstrated the effectiveness of incorporation of prior knowledge into SimpleMKKM.

Method and software for processing daily EEG signals for detection of epileptic seizures in humans

A method, an algorithm and a software tool for processing daily EEG signals for computer electroencephalographic systems to detect the manifestation of epileptic seizures in humans have been developed. Mathematically, the daily EEG signal is presented as a random sequence of white Gaussian noise zones and additive mixtures of different-frequency harmonic components. Harmonic functions interpret the manifestations of epileptic seizures. The core of the method of processing daily EEG signals is a time-shifted window inter-covariance processing with multiple kernels in the form of different-frequency harmonic functions. Based on the method of window processing, an algorithm and a software tool for daily EEG signal processing with a graphical user interface using the MATLAB environment have been implemented. The developed software can be used as a component of computer EEG systems. The results of daily EEG signal processing using the software are displayed in the form of averaged products of covariance results (the value is measured in power units) within each processing window, which quantitatively reflect the time points of epileptic seizures in a person. Manifestations of epileptic seizures are reflected through the increase in the averaged values of the power of covariances in relation to observation intervals without corresponding manifestations of these seizures. To ensure the authorization of the process of determining the level of decision-making regarding the moments of epileptic seizures (exceeding the normal level), the threshold algorithm and the Neumann-Pearson statistical criterion were applied.

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

MK-MSMCR: An efficient multiple kernel approach to multi-class classification

This paper introduces a novel multi-class support vector classification and regression (MSVCR) algorithm with multiple kernel learning (MK-MSVCR). We present a new MK-MSVCR algorithm based on two-stage learning (MK-MSVCRTSL). The two-stage learning aims to make classification algorithms better when dealing with complex data by using the first stage of learning to generate ?representative? or ?important? samples. We first establish the fast learning rate of MKMSVCR algorithm for multi-class classification with independent and identically distributed (i.i.d.) samples amd uniformly ergodic Markov chain (u.e.M.c.) smaples, and prove that MK-MSVCR algorithm is consistent. We show the numerical investigation on the learning performance of MK-MSVCR-TSL algorithm. The experimental studies indicate that the proposed MK-MSVCR-TSL algorithm has better learning performance in terms of prediction accuracy, sampling and training total time than other multi-class classification algorithms.

Novel generalized inequalities involving a general Hardy operator with multiple variables and general kernels on time scales

Novel generalized inequalities involving a general Hardy operator with multiple variables and general kernels on time scales

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations.

Prior methodologies have disregarded the diversities among distinct degradation types during image reconstruction, employing a uniform network model to handle multiple deteriorations. Nevertheless, we discover that prevalent degradation modalities, including sampling, blurring, and noise, can be roughly categorized into two classes. We classify the first class as spatial-agnostic dominant degradations, less affected by regional changes in image space, such as downsampling and noise degradation. The second class degradation type is intimately associated with the spatial position of the image, such as blurring, and we identify them as spatial-specific dominant degradations. We introduce a dynamic filter network integrating global and local branches to address these two degradation types. This network can greatly alleviate the practical degradation problem. Specifically, the global dynamic filtering layer can perceive the spatial-agnostic dominant degradation in different images by applying weights generated by the attention mechanism to multiple parallel standard convolution kernels, enhancing the network's representation ability. Meanwhile, the local dynamic filtering layer converts feature maps of the image into a spatially specific dynamic filtering operator, which performs spatially specific convolution operations on the image features to handle spatial-specific dominant degradations. By effectively integrating both global and local dynamic filtering operators, our proposed method outperforms state-of-the-art blind super-resolution algorithms in both synthetic and real image datasets.