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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Ling Ding Hongyi Li Changmiao Hu Wei Zhang Shumin Wang Institute of Earthquake Forecasting China Earthquake Administration Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression,an exploratory research is done on feature extraction and classification for high resolution remote sensing *** the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example,the image segmentation object was taken as the basic unit,and the pre-trained AlexN et deep convolution neural network model was used for feature *** the spectral features,AlexN et features and GLCM texture features are combined with multi-kernel learning and SVM classifier,finally the classification results were compared and *** results show that the deep convolution neural network can extract more accurate remote sensing image features,and significantly improve the overall accuracy of classification,and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

关键词： AlexNet GLCM texture multi-kernel learning Object-oriented classification feature extraction SVM

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ONLINE multi-kernel learning WITH ORTHOGONAL RANDOM FEATURES

ONLINE MULTI-KERNEL LEARNING WITH ORTHOGONAL RANDOM FEATURES

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yanning Shen Tianyi Chen Georgios B. Giannakis Dept. of ECE and DTC University of Minnesota Minneapolis USA

ISBN: (纸本)9781538646595

kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly.' Performance analysis shows that the novel algorithm can afford sublinear regret. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.

关键词： multi-kernel learning random features online learning online regression e-learning Orthogonal learning Seeds kernel

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multi-kernel extreme learning machine for EEG classification in brain-computer interfaces

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 96卷 302-310页

作者： Zhang, Yu Wang, Yu Zhou, Guoxu Jin, Jing Wang, Bei Wang, Xingyu Cichocki, Andrzej East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai Peoples R China Shanghai Ruanzhong Informat Technol Co Ltd Shanghai Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama Japan Skolkovo Inst Sci & Technol SKOLTECH Moscow 143026 Russia

One of the most important issues for the development of a motor-imagery based brain-computer interface (BCI) is how to design a powerful classifier with strong generalization capability. Extreme learning machine (ELM) has recently proven to be comparable or more efficient than support vector machine for many pattern recognition problems. In this study, we propose a multi-kernel ELM (MKELM)-based method for motor imagery electroencephalogram (EEG) classification. The kernel extension of ELM provides an elegant way to circumvent calculation of the hidden layer outputs and inherently encode it in a kernel matrix. We investigate effects of two different kernel functions (i.e., Gaussian kernel and polynomial kernel) on the performance of kernel ELM. The MKELM method is subsequently developed by integrating these two types of kernels with a multi-kernel learning strategy, which can effectively explore the supplementary information from multiple nonlinear feature spaces for more robust classification of EEG. An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms. The experimental results confirm that superiority of the proposed MKELM-based method for accurate classification of EEG associated with motor imagery in BCI applications. Our method also provides a promising and generalized solution to investigate the complex and nonlinear information for various applications in the fields of expert and intelligent systems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Brain-computer interface Electroencephalogram Extreme learning machine multi-kernel learning Motor imagery

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Random feature-based online multi-kernel learning in environments with unknown dynamics

The Journal of Machine Learning Research

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The Journal of Machine learning Research 2019年第1期20卷

作者： Yanning Shen Tianyi Chen Georgios B. Giannakis Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN

kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multikernel learning has gained popularity thanks to its "exibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a scalable multikernel learning scheme (termed Raker) to obtain the sought nonlinear learning function 'on the fly,' first for static environments. To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed. AdaRaker accounts not only for data-driven learning of kernel combination, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, and with with analytic performance guarantees. Tests with synthetic and real datasets are carried out to showcase the effectiveness of the novel algorithms.

关键词： dynamic and adversarial environments multi-kernel learning online learning random features reproducing kernel Hilbert space

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Semi-supervised multi-kernel Extreme learning Machine

Semi-supervised Multi-kernel Extreme Learning Machine

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6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Abuassba, Adnan O. M. Zhang Dezheng Mahmood, Zahid Univ Sci & Technol Dept Comp Sci & Commun Engn Beijing Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN). It expanded to semi-supervised ELM (SSELM) to deal with unlabeled data problem. In such a problem, labeled data is either rare or not cheap. Although SSELM has a good generalization performance, it might be influenced by heterogeneous data from different sources. It is discernible that unbalanced data issue inflicts obstacles in real-world applications including medical diagnostics and credit card fraud detection. To deal with this issue in this paper, we introduce a multi-kernel semi-supervised ELM (MKSSELM). It is more flexible to deal with discrete data from various sources. It matches diverse information from disparate sources and it shows distinction among the data. Instead of using one kernel, we optimize both ELM structural parameters and kernel combination weights. The optimization process accomplished by commanding an L1-norm as a regulation term. Meanwhile, a non-negative constraint on the kernel combination weights is used. The validity of MKSSELM algorithm is confirmed through classification results on real-world benchmark datasets. The proposed algorithm achieved better or comparable results with respect to previous approaches. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Classification kernel learning Semi-supervised learning multi-kernel learning Extreme learning Machine

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Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

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Frontiers of Computer Science 2017年第2期11卷 276-289页

作者： Lele CAO Fuchun SUN Hongbo LI Wenbing HUANG State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Department of Computing and Information Systems The University of Melbourne Parkville 3010 VIC Australia

Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine （OM-ELM） which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning （MKL） problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.

关键词： multi-kernel learning online learning extreme learning machine feature fusion robot recognition

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Semi-supervised multi-kernel Extreme learning Machine

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Procedia Computer Science 2018年 129卷 305-311页

作者： Adnan OM Abuassba Zhang Dezheng Zahid Mahmood Department of Computer Science and Communication Engineering University of Science and Technology and Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083 China

关键词： Classification kernel learning Semi-supervised learning multi-kernel learning Extreme learning Machine

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Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1278-1286页

作者： Wang, Jim Jing-Yan Huang, Jianhua Z. Sun, Yijun Gao, Xin KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Texas A&M Univ Dept Stat College Stn TX 77843 USA SUNY Buffalo Buffalo NY 14203 USA

Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMF(FS) and AGNMF(MK), by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： Data representation Nonnegative matrix factorization Graph regularization Feature selection multi-kernel learning

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multi-kernel partial label learning using graph contrast disambiguation

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APPLIED INTELLIGENCE 2024年第20期54卷 9760-9782页

作者： Li, Hongyan Wan, Zhonglin Vong, Chi Man Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China Dongguan City Coll Sch Artificial Intelligence Dongguan Guangdong Peoples R China Dongguan Polytech Sch Econ & Management Dongguan Guangdong Peoples R China

Partial label learning (PLL) handles data classification problems by assigning a candidate label set to each sample. There is always one correct label in a candidate label set. Since the PLL can achieve classification without precise labels, it can reduce the cost of data annotation. However, the PLL suffers from challenges and difficulties caused by the ambiguity of the candidate labels. Most PLL algorithms eliminate the ambiguity in candidate label sets by treating each candidate label equally or iteratively identifying the ground-truth label without employing optimization kernels, whereas the optimal kernel can ensure better performance for PLL tasks. Moreover, there is no general framework to handle heterogeneous data classification in various applications. Inspired by the successful application of multi-kernel learning in machine learning, this paper integrates multi-kernel learning into the PLL framework to develop a new multi-kernel PLL (PL-MKL) algorithm, which adopts different kernels to map the original sample attributes to distinct nonlinear feature spaces. Accordingly, the model's classification performance can be enhanced by joining the mapping capabilities of multiple feature spaces while fully exploring the intrinsic distribution of data. Furthermore, the PL-MKL combines similarity and dispersion graphs to develop an innovative method based on graph contrast disambiguation. This approach maintains the manifold characteristics of the data and reflects the differences in candidate labels, thus alleviating intra-class differences while gaining inter-class ones. An efficient optimization algorithm is proposed to attain the underlined objectives. Extensive experiments demonstrate the competitive or superior performance of the suggested PL-MKL over state-of-the-art approaches.

关键词： Partial label learning multi-kernel learning Dispersion graph

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multi-kernel METRIC learning FOR PERSON RE-IDENTIFICATION 23

MULTI-KERNEL METRIC LEARNING FOR PERSON RE-IDENTIFICATION

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Syed, Muhammad Adnan Jiao, Jianbin Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

ISBN: (纸本)9781467399616

In this paper, we propose a new multi-kernel Metric learning (MKML) approach to enhance the performance of person re-identification using adaptive weighted multi-kernel. The intuition behind our approach is that different features, i.e., low-level and middle-level features, have different nature and thus discriminating capability, utilizing different kernels could map these features into sub-spaces, which helps to improve the discrimination among features. The kernels are combined with an adaptive weighting strategy to get an efficient kernel space. The Fisher Discriminant Analysis (FDA) is used to learn the metric in the learned weighted kernels space that enhances the robustness of metric to discriminate among classes. Experiments on two challenging person re identification datasets, i.e., VIPeR and CUHK01, demonstrated that our approach is effective.

关键词： Person Re-identification multi-kernel learning Metric learning

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