检索结果-内蒙古大学图书馆

multi-kernel learning for Heterogeneous Data

IEEE ACCESS 2025年 13卷 45340-45349页

作者： Liao, Chunlan Peng, Shili Guangzhou Panyu Polytech Guangzhou 511483 Peoples R China Guangdong Univ Finance Dept Internet Finance Guangzhou 510521 Peoples R China

multi-kernel learning is an excellent machine learning algorithm widely used in various learning tasks such as classification and regression. Traditional kernel methods mainly focus on numerical data and lack sufficient research on categorical and mixed data. However, mixed data is widely used in practical applications, and many unstructured data can be converted into mixed data through appropriate preprocessing. In this work, we propose a new Heterogeneous multi-kernel learning (HMKL) algorithm for processing mixed data containing both categorical and numerical attributes. In HMKL, category attributes and numerical attributes are processed separately. Different similarity measurement methods are used to obtain different kernel matrices for category attributes, which are then fused with numerical kernel matrices to improve the classification performance of multi-kernel learning. We propose a new ratio Gaussian kernel function for category attributes, which can maintain a balance between the AND and OR operations of the matching kernel matrix. In addition, to address the curse of dimensionality caused by one-of-N encoding, we use the summation of matching kernel matrices to reduce the difficulty of preprocessing categorical attributes. The experiment shows that our proposed HMKL algorithm can effectively handle mixed data and has excellent performance.

关键词： kernel Encoding Distance measurement Support vector machines Training Symbols Regression tree analysis Hamming distances Data processing Transforms multi-kernel learning support vector machine categorical data mixed data kernel methods

来源：评论

学校读者我要写书评

暂无评论

An Efficient Adaptive multi-kernel learning With Safe Screening Rule for Outlier Detection

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3656-3669页

作者： Wang, Xinye Duan, Lei He, Chengxin Chen, Yuanyuan Wu, Xindong Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Zhejiang Lab Res Ctr Knowledge Engn Hangzhou 311121 Peoples R China

Recent advances in multi-kernel-based methods for outlier detection have positioned them as an attractive way to detect instances that are markedly different from the remaining data in a dataset. Currently, most outlier detection approaches based on multi-kernel learning are simply a convex combination of various kernels with handcrafted weights, meaning that these weights may not be suitable. Meanwhile, this combination of weights does not sufficiently consider the intrinsic correlations of instances when fusing different kernels. Thus, a key challenge is how to adaptively learn an appropriate combination of weights for capturing a new feature space in which outliers can be better detected than the original space. Simultaneously, it is still a burning issue to get the optimal combination of weights due to considerable computational cost and memory usage when the feature or instance size is large. In this paper, we propose a novel method for efficient adaptive multi-kernel for outlier detection (EAMOD), which automatically learns the optimal weight for each training instance under different kernels using a non-negative function. In addition, we design a safe screening rule (SSR) for EAMOD to improve its training efficiency without any loss of accuracy. To the best of our knowledge, it is the first attempt to develop SSR for multi-kernel-based outlier detection methods. Extensive experiments show that EAMOD is effective and efficient.

关键词： multi-kernel learning safe screening rule (SSR) support vector data description (SVDD) outlier detection multi-kernel learning safe screening rule (SSR) support vector data description (SVDD) outlier detection

来源：评论

学校读者我要写书评

暂无评论

Auto-Weighted multi-View Deep Non-Negative Matrix Factorization With multi-kernel learning

引用

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2025年 11卷 23-34页

作者： Yang, Xuanhao Che, Hangjun Leung, Man-Fai Liu, Cheng Wen, Shiping Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge CB1 1PT England Shantou Univ Dept Comp Sci Shantou 515063 Guangdong Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst Fac Engn & Informat Technol Sydney NSW 2007 Australia

Deep matrix factorization (DMF) has the capability to discover hierarchical structures within raw data by factorizing matrices layer by layer, allowing it to utilize latent information for superior clustering performance. However, DMF-based approaches face limitations when dealing with complex and nonlinear raw data. To address this issue, Auto-weighted multi-view Deep Nonnegative Matrix Factorization with multi-kernel learning (MvMKDNMF) is proposed by incorporating multi-kernel learning into deep nonnegative matrix factorization. Specifically, samples are mapped into the kernel space which is a convex combination of several predefined kernels, free from selecting kernels manually. Furthermore, to preserve the local manifold structure of samples, a graph regularization is embedded in each view and the weights are assigned adaptively to different views. An alternate iteration algorithm is designed to solve the proposed model, and the convergence and computational complexity are also analyzed. Comparative experiments are conducted across nine multi-view datasets against seven state-of-the-art clustering methods showing the superior performances of the proposed MvMKDNMF.

关键词： kernel Data models Matrix decomposition Vectors Manifolds Information processing Clustering algorithms Adaptation models Optimization Computational modeling multi-view clustering deep matrix factorization multi-kernel learning

来源：评论

学校读者我要写书评

暂无评论

multi-Modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse multi-kernel learning and Distribution Adaption

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第4期20卷 2387-2397页

作者： Zhang, Yuanpeng Xia, Kaijian Jiang, Yizhang Qian, Pengjiang Cai, Weiwei Qiu, Chengyu Lai, Khin Wee Wu, Dongrui Nantong Univ Med Nursing Sch Dept Med Informat Nantong 226001 Jiangsu Peoples R China Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur 50603 Malaysia Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Huazhong Univ Sci & Technol Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Hubei Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Cent South Univ Forestry & Technol Sch Logist & Transportat Changsha 410004 Peoples R China AiTech Artificial Intelligence Res Inst Changsha 410000 Peoples R China

With the development of sensors, more and more multimodal data are accumulated, especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis becomes very important and urgent. In this study, we combine multi-kernel learning and transfer learning, and propose a feature-level multi-modality fusion model with insufficient training samples. To be specific, we firstly extend kernel Ridge regression to its multi-kernel version under the l(p)-norm constraint to explore complementary patterns contained in multimodal data. Then we use marginal probability distribution adaption to minimize the distribution differences between the source domain and the target domain to solve the problem of insufficient training samples. Based on epilepsy EEG data provided by the University of Bonn, we construct 12 multi-modality & transfer scenarios to evaluate our model. Experimental results show that compared with baselines, our model performs better on most scenarios.

关键词： multi-kernel learning transfer learning multi-modality fusion EEG manifold regularization

来源：评论

学校读者我要写书评

暂无评论

Graph-Aided Online multi-kernel learning

引用

JOURNAL OF MACHINE learning RESEARCH 2023年第1期24卷 1-44页

作者： Ghari, Pouya M. Shen, Yanning Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

multi-kernel learning (MKL) has been widely used in learning problems involving function learning tasks. Compared with single kernel learning approach which relies on a pre-selected kernel, the advantage of MKL is its flexibility results from combining a dictionary of kernels. However, inclusion of irrelevant kernels in the dictionary may deteriorate the accuracy of MKL, and increase the computational complexity. Faced with this challenge, a novel graph-aided framework is developed to select a subset of kernels from the dictionary with the assistance of a graph. Different graph construction and refinement schemes are developed based on incurred losses or kernel similarities to assist the adaptive selection process. Moreover, to cope with the scenario where data may be collected in a sequential fashion, or cannot be stored in batch due to the massive scale, random feature approximation are adopted to enable online function learning. It is proved that our proposed algorithms enjoy sub-linear regret bounds. Experiments on a number of real datasets showcase the advantages of our novel graph-aided algorithms compared to state-of-the-art alternatives. 1

关键词： multi-kernel learning Graphs Random Features Function Approximation Online learning

来源：评论

学校读者我要写书评

暂无评论

A Stochastic Configuration Network with Attenuation Regularization and multi-kernel learning and Its Application 36

A Stochastic Configuration Network with Attenuation Regulari...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Zang, Xinyan Yan, Aijun Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Univ Technol Beijing Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

To improve the generalization and robustness of the stochastic configuration network (SCN) model, an improved SCN algorithm is proposed. Attenuated L-2 regularization is added to the iterative solution process of the output weight, which not only reduces the output weight of the hidden layer nodes in the early iteration, but also ensures that the new nodes after multiple iterations are not affected by the larger regularization coefficient, forming the constraint effect of first tight and then loose. In addition, the multi-kernel learning method is introduced to obtain the characteristics of the sample data, and the penalty weight matrix of the training samples is determined, so as to re-evaluate the output weight of the SCN model to enhance the robustness of the model. The performance of the proposed method was tested on four standard datasets and historical data from municipal solid waste incineration processes. The experimental results show that the modeling method proposed in this paper has certain advantages in improving the model's generalization and robustness.

关键词： stochastic configuration network attenuation regularization multi-kernel learning generalization robustness municipal solid waste incineration

来源：评论

学校读者我要写书评

暂无评论

Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on multi-kernel learning

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3517-3529页

作者： Zhou, Haohao Wang, Hao Tang, Jijun Ding, Yijie Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Univ Elect Sci & Technol China Yangtze Delta Region Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

Non-coding RNAs (ncRNAs) are a type of RNAs which are not used to encode protein sequences. Emerging evidence shows that lots of ncRNAs may participate in many biological processes and must be widely involved in many types of cancers. Therefore, understanding their functionality is of great importance. Similar to proteins, various functions of ncRNAs relies on their subcellular localizations. Traditional high-throughput methods in wet-lab to identify subcellular localization is time-consuming and costly. In this paper, we propose a novel computational method based on multi-kernel learning to identify multi-label ncRNA subcellular localizations, via graph regularized k-local hyperplane distance nearest neighbor algorithm. First, we construct six types of sequencebased feature descriptors and select important feature vectors. Then, we build a multi-kernel learning model with Hilbert-Schmidt independence criterion (HSIC) to obtain optimal weights for vairous features. Furthermore, we propose the graph regularized k-local hyperplane distance nearest neighbor algorithm (GHKNN) as a binary classification model for detecting one kind of non-coding RNA subcellular localization. Finally, we apply One-vs-Rest strategy to decompose multi-label problem of non-coding RNA subcellular localizations. Our method achieves excellent performance on three ncRNA datasets and three human ncRNA datasets, and outperforms other outstanding machine learning methods. Comparing to existing method, our model also performs well especially on small datasets. We expect that this model will be useful for the prediction of subcellular localization and the study of important functional mechanisms of ncRNAs. Furthermore, we establish user-friendly web server (http://***/) with the implementation of our method, which can be easily used by most experimental scientists.

关键词： RNA Location awareness Feature extraction Proteins kernel Databases Biological information theory Non-coding RNA subcellular localization multi-label classification multi-kernel learning k-local hyperplane distance nearest neighbor

来源：评论

学校读者我要写书评

暂无评论

A New L1 multi-kernel learning Support Vector Regression Ensemble Algorithm With AdaBoost

引用

IEEE ACCESS 2022年 10卷 20375-20384页

作者： Xie, Xiaojin Luo, Kangyang Wang, Guoqiang Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China East China Normal Univ Sch Data Sci & Engn Shanghai 200062 Peoples R China

This paper proposes a new multi-kernel learning ensemble algorithm, called Ada-L1MKL-WSVR, which can be regarded as an extension of multi-kernel learning (MKL) and weighted support vector regression (WSVR). The first novelty is to add the L-1 norm of the weights of the combined kernel function to the objective function of WSVR, which is used to adaptively select the optimal base models and their parameters. In addition, an accelerated method based on fast iterative shrinkage thresholding algorithm (FISTA) is developed to solve the weights of the combined kernel function. The second novelty is to propose an integrated learning framework based on AdaBoost, named Ada-L1MKL-WSVR. In this framework, we integrate FISTA into AdaBoost. At each iteration, we optimize the weights of the combined kernel function and update the weights of the training samples at the same time. Then an ensemble regression function of a set of regression functions is output. Finally, two groups of the experiments are designed to verify the performance of our algorithm. On the first group of the experiments including eight datasets from UCI machine learning repository, the MAEs and RMSEs of Ada-L1MKL-WSVR are reduced by 11.14% and 9.08% on average, respectively. Furthermore, on the second group of the experiments including the COVID-19 epidemic datasets from eight countries, the MAEs and RMSEs of Ada-L1MKL-WSVR are reduced by 31.19% and 29.98% on average, respectively.

关键词： Support vector regression multi-kernel learning AdaBoost ensemble algorithm regression prediction

来源：评论

学校读者我要写书评

暂无评论

multi-kernel learning for multi-label classification with local Rademacher complexity

引用

INFORMATION SCIENCES 2023年第1期647卷

作者： Wang, Zhenxin Chen, Degang Che, Xiaoya North China Elect Power Univ Sch Control & Comp Engn Beijing 100206 Peoples R China North China Elect Power Univ Sch Math & Phys Beijing 100206 Peoples R China

multi-label classification aims to construct prediction models from input space to output space for multi-label datesets. However, the feature space of multi-label dataset and the hypothesis space of classifier are always complex. In order to obtain high performance multi-kernel learning algorithms, the core problem is to design algorithms that compress both hypothesis space and feature space. In this paper, by combining the local Rademacher complexity and Hilbert-Schmidt independence criterion (HSIC), an effective multi-kernel learning algorithm for multi-label classification is proposed to compress the feature space and hypothesis space simultaneously. Based on the tail sum of eigenvalues of the integral operator corresponding to kernels, the upper bound of local Rademacher complexity of linear functions class in the feature space is determined. The Hilbert-Schmidt independence criterion is applied to maximize the correlation between the convex combination of the input kernel matrices and the ideal kernel matrix in the label space. Finally, the Laplace regularization model is built by controlling the tail sum of eigenvalues of the integral operator corresponding to kernels of this criterion. Therefore, the above process obtains a combined kernel, simultaneously by sorting the coefficients of the combined kernel, a feature selection algorithm can be constructed. On the basis, the combined kernel can also be used to design a new binary classifier for multi-label classification. The experimental results verify that our proposed multi-kernel learning algorithms can effectively compress the hypothesis space, while the feature selection algorithms can effectively compress the feature space.

关键词： multi-label classification multi-kernel learning Local Rademacher complexity Feature selection Hilbert-Schmidt independence criterion

来源：评论

学校读者我要写书评

暂无评论

GRAPH NEURAL NETWORK WITH multi-kernel learning FOR multiSPECTRAL POINT CLOUD CLASSIFICATION

GRAPH NEURAL NETWORK WITH MULTI-KERNEL LEARNING FOR MULTISPE...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Zifeng Wang, Qingwang Wang, Mingye Shen, Tao Kunming Univ Sci & Technol Kunming Yunnan Peoples R China Yunnan Key Lab Comp Technol Applicat Kunming Yunnan Peoples R China

ISBN: (纸本)9798350320107

multispectral point clouds provide the data basis for finer land cover classification due to the simultaneous spatial and spectral information. How to jointly utilize spatial-spectral information becomes a hot research direction. Benefiting from the excellent performance of graph neural networks ( GNNs) on non-Euclidean data, it is well suited to modelling multispectral point clouds to achieve higher classification accuracy. This paper proposes a novel graph convolutional networks with multi-kernel learning (GCN-MKL) for adaptively constructing a graph of multispectral point cloud for finer classification. Specifically, we use multiple base kernels to map the multispectral point cloud into a high-dimensional feature space and learn a linear combination of base kernels through a multi-kernel learning mechanism embedded in the network. The learned multikernel graph can effectively measure the high-dimensional similarity between multispectral points. Experimental results demonstrate that the proposed GCN-MKL outperforms several state-of-the-art methods on a real multispectral point cloud.

关键词： multispectral LiDAR data graph neural network multi-kernel learning point cloud classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：