Multiple Kernel LSSVM in Empirical Kernel Mapping Space

2009 ◽  
Author(s):  
Bo Yang ◽  
Yingyong Bu
Keyword(s):  
Mapping Space ◽  
Multiple Kernel ◽  
Empirical Kernel Mapping ◽  
Kernel Mapping

Multiple Kernel Feature Fusion Using Kernel Fisher Method

2013 ◽  
Vol 333-335 ◽  
pp. 1406-1409
Author(s):  
Bo Yang
Keyword(s):  
Feature Fusion ◽  
Kernel Method ◽  
Kernel Space ◽  
Mapping Space ◽  
Weighted Fusion ◽  
Multiple Kernel ◽  
Fisher Method ◽  
Empirical Kernel Mapping ◽  
Kernel Classification ◽  
Kernel Mapping

Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel mapping samples. Based this fact, we developed a multiple kernel Fisher method to realize multiple kernel classification in empirical kernel mapping space. The experiments here illustrate that the proposed multiple kernel fisher method is feasible and effective.

scholarly journals Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM+

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Xinying Yu ◽  
Bo Peng ◽  
Zeyu Xue ◽  
Hamidreza Saligheh Rad ◽  
Zhenlin Cai ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Structural Changes ◽  
Early Period ◽  
Brain Network ◽  
Mr Images ◽  
Matter Volume ◽  
Brain Mr Images ◽  
Empirical Kernel Mapping ◽  
Structural Brain ◽  
Kernel Mapping

Abstract Background Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine disease status, prognosis assessment, guidance and management, but is not well studied in the framework of machine learning. Methods We proposed empirical kernel mapping-based kernel extreme learning machine plus (EKM–KELM+) classifier to discriminate different blood pressure grades in adults from structural brain MR images. ELM+ is the extended version of ELM, which integrates the additional privileged information about training samples in ELM to help train a more effective classifier. In this work, we extracted gray matter volume (GMV), white matter volume, cerebrospinal fluid volume, cortical surface area, cortical thickness from structural brain MR images, and constructed brain network features based on thickness. After feature selection and EKM, the enhanced features are obtained. Then, we select one feature type as the main feature to feed into KELM+, and the rest of the feature types are PI to assist the main feature to train 5 KELM+ classifiers. Finally, the 5 KELM+ classifiers are ensemble to predict classification result in the test stage, while PI is not used during testing. Results We evaluated the performance of the proposed EKM–KELM+ method using four grades of hypertension data (73 samples for each grade). The experimental results show that the GMV performs observably better than any other feature types with a comparatively higher classification accuracy of 77.37% (Grade 1 vs. Grade 2), 93.19% (Grade 1 vs. Grade 3), and 95.15% (Grade 1 vs. Grade 4). The most discriminative brain regions found using our method are olfactory, orbitofrontal cortex (inferior), supplementary motor area, etc. Conclusions Using region of interest features and brain network features, EKM–KELM+ is proposed to study the most discriminative regions that have obvious structural changes in different blood pressure grades. The discriminative features that are selected using our method are consistent with the existing neuroimaging studies. Moreover, our study provides a potential approach to take effective interventions in the early period, when the blood pressure makes minor impacts on the brain structure and function.

Kernelizing the Proportional Odds Model through the Empirical Kernel Mapping

2013 ◽  
pp. 270-279
Author(s):  
María Pérez-Ortiz ◽  
Pedro Antonio Gutiérrez ◽  
Manuel Cruz-Ramírez ◽  
Javier Sánchez-Monedero ◽  
Cesar Hervás-Martínez
Keyword(s):  
Proportional Odds Model ◽  
Proportional Odds ◽  
Empirical Kernel Mapping ◽  
Kernel Mapping

Learning optimal kernel mapping based on function space with dynamic parameters

2013 ◽  
Vol 33 (8) ◽  
pp. 2337-2340
Author(s):  
Zhiying TAN ◽  
Ying CHEN ◽  
Yong FENG ◽  
Xiaobo SONG
Keyword(s):  
Function Space ◽  
Dynamic Parameters ◽  
Optimal Kernel ◽  
Kernel Mapping

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

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