Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete da...Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.展开更多
Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assum...Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.展开更多
Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-ba...Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.展开更多
As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarde...As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.展开更多
For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into ...For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.展开更多
Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multi...Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.展开更多
An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquir...An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.展开更多
A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set l...A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.展开更多
This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rat...This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.展开更多
The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map l...The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.展开更多
Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as f...Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.展开更多
基金funded by National Natural Science Foundation of China under Grant Nos.61972057 and U1836208Hunan Provincial Natural Science Foundation of China under Grant No.2019JJ50655+3 种基金Scientic Research Foundation of Hunan Provincial Education Department of China under Grant No.18B160Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)under Grant No.kfj180402the“Double First-class”International Cooperation and Development Scientic Research Project of Changsha University of Science and Technology under Grant No.2018IC25the Researchers Supporting Project No.(RSP-2020/102)King Saud University,Riyadh,Saudi Arabia.
文摘Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.
基金This work was supported by the National Key R&D Program of China(No.2018YFB1003203)National Natural Science Foundation of China(Nos.61672528,61773392,61772561)+1 种基金Educational Commission of Hu Nan Province,China(No.14B193)the Key Research&Development Plan of Hunan Province(No.2018NK2012).
文摘Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62072385,62172076,and U22A2038)the Municipal Government of Quzhou(2022D040)the Zhejiang Provincia1l Natural Science Foundationof China(No.LY23F020003).
文摘Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.
文摘As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.
基金Supported by the National Natural Science Foundation of China(51467008).
文摘For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.
基金supported by National Natural Science Foundation of China(No.62176054)University Synergy Innovation Program of Anhui Province,China(No.GXXT-2020-015)。
文摘Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.
基金supported by the National Natural Science Foundation of China(Grant No.52071216)the Shanghai Rising-Star Program.
文摘An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.
基金supported by the National Natural Science Key Foundation of China(69974021)
文摘A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.
基金supported by National Natural Science Foundation of China(Nos.61300163,61125106 and 61300162)Jiangsu Key Laboratory of Big Data Analysis Technology
文摘This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.
基金supported by the National Natural Science Foundation of China (Grant Nos.61922020,61771331,61902259).
文摘The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.
基金Supported by the National Natural Science Foundation of China(Nos. 30625012 and 60721003)
文摘Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.
