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An Enhanced semi-supervised support vector machine Algorithm for Spectral-Spatial Hyperspectral Image Classification

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第1期34卷 199-211页

作者： He, Ziping Xia, Kewen Zhang, Jiangnan Wang, Sijie Yin, Zhixian Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

Hyperspectral image classification has become an important issue in remote sensing due to the significant amount of spectral information in HSI. The costly and time-consuming annotation task of HSIs makes the number of labeled samples is limited. To address the above problem, we propose an enhanced semi-supervised support vector machine algorithm for spectral-spatial HSI classification. To fully capture the spectral and spatial information of HSI, we use local binary pattern to obtain spatial feature. The captured spatial features are concatenated with the spectral features to yield the hybrid spectral-spatial features. Self-training mechanism is then adopted to gradually select confident unlabeled samples with their pseudo-labels and add them to the labeled set. To further improve the classification performance of the semi-supervised support vector machine, we choose a cuckoo search algorithm based on the chaotic catfish effect to find its optimal combination of parameters. The experimental results on two publicly available HSI datasets show that the proposed model achieves excellent classification accuracy for each category in hyperspectral images, and also has superior overall accuracy compared with other comparative algorithms. Adequate experiments and analysis illustrate the promising potential and prospect of our proposed model for HSI classification.

关键词： hyperspectral image classification semi-supervised support vector machine self-training cuckoo search algorithm chaotic catfish effect

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A P-ADMM for sparse quadratic kernel-free least squares semi-supervised support vector machine

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NEUROCOMPUTING 2018年 306卷 37-50页

作者： Zhan, Yaru Bai, Yanqin Zhang, Wei Ying, Shihui Shanghai Univ Dept Math Shanghai 200444 Peoples R China

In this paper, we propose a sparse quadratic kernel-free least squares semi-supervised support vector machine model by adding an L-1 norm regularization term to the objective function and using the least squares method, which results in a nonconvex and nonsmooth quadratic programming problem. For computational considerations, we use the smoothing technique and consensus technique. Then we adopt the proximal alternating direction method of multipliers (P-ADMM) to solve it, as well as propose a strategy of parameter selection. Then we not only derive the convergence analysis of algorithm, but also estimate the convergence rate as o(1/root k), where k is the number of iteration. This gives the best bound of P-ADMM known so far for nonconvex consensus problem. To demonstrate the efficiency of our model, we compare the proposed method with several state-of-the-art methods. The numerical results show that our model can achieve both better accuracy and sparsity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： semi-supervised support vector machine Sparsity Quadratic kernel-free least square Smoothing Consensus model P-ADMM

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New Incremental Learning Algorithm for semi-supervised support vector machine 18

New Incremental Learning Algorithm for Semi-Supervised Suppo...

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24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Gu, Bin Yuan, Xiao-Tong Chen, Songcan Huang, Heng Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA Nanjing Univ Informat Sci Technol Sch Informat & Control Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781450355520

semi-supervised learning is especially important in data mining applications because it can make use of plentiful unlabeled data to train the high-quality learning models. semi-supervised support vector machine (S3VM) is a powerful semi-supervised learning model. However, the high computational cost and non-convexity severely impede the S3VM method in large-scale applications. Although several learning algorithms were proposed for S3VM, scaling up S3VM is still an open problem. To address this challenging problem, in this paper, we propose a new incremental learning algorithm to scale up S3VM (IL-S3VM) based on the path following technique in the framework of Difference of Convex (DC) programming. The traditional DC programming based algorithms need multiple outer loops and are not suitable for incremental learning, and traditional path following algorithms are limited to convex problems. Our new IL-S3VM algorithm based on the path-following technique can directly update the solution of S3VM to converge to a local minimum within one outer loop so that the efficient incremental learning can be achieved. More importantly, we provide the finite convergence analysis for our newalgorithm. To the best of our knowledge, our new IL-S3VM algorithm is the first efficient path following algorithm for a non-convex problem (i.e., S3VM) with local minimum convergence guarantee. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-S3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.

关键词： semi-supervised support vector machine incremental learning path following algorithm difference of convex programming

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An overview on semi-supervised support vector machine

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NEURAL COMPUTING & APPLICATIONS 2017年第5期28卷 969-978页

作者： Ding, Shifei Zhu, Zhibin Zhang, Xiekai China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Peoples R China

support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies to supervised learning. Large amount of data generated in real life is unlabeled, and the standard form of SVM cannot make good use of these data to improve its learning ability. However, semi-supervised support vector machine (S3VM) is a good solution to this problem. This paper reviews the recent progress in semi-supervised support vector machine. First, the basic theory of S3VM is expounded and discussed in detail;then, the mainstream model of S3VM is presented, including transductive support vector machine, Laplacian support vector machine, S3VM training via the label mean, S3VM based on cluster kernel;finally, we give the conclusions and look ahead to the research on S3VM.

关键词： semi-supervised support vector machine semi-supervised support vector machine

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A New Classification Method Based on semi-supervised support vector machine 1

A New Classification Method Based on Semi-supervised Support...

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1st International Conference on Human Centered Computing (HCC)

作者： Jiang, Weijin Yao Lina Jiang Xinjun Xu Yuhui Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China Hunan Univ Commerce Sch Comp & Informat Engn Changsha Hunan Peoples R China Hunan Radio & TV Univ Dept Comp Changsha 410005 Hunan Peoples R China

ISBN: (纸本)9783319155548;9783319155531

semi-supervised learning using tag vector machine is a relatively new method of data classification and label-free. semi-supervised support vector machines model the objective function is not smooth and fast optimization algorithm to solve the model cannot be applied. This paper presents a general three-moment method 3 times differentiable at the origin of construct quintic spline functions, construction of hinge can be used to approximate symmetry loss functions, the approximate accuracy estimation of and quintic spline functions. And on top of this, deduced five and a half times b-spline smoothing support vector machines for non-smooth a-smoothing model analyses the convergence. Broyden-Fletcher-Goldfarb-Shanno (storage) algorithm can be used in new models. Experimental results show that the new model has a better performance.

关键词： Classification algorithm Smooth Spline unction semi-supervised support vector machine

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Towards Practical Large Scale Non-Linear semi-supervised Learning with Balancing Constraints 22

Towards Practical Large Scale Non-Linear Semi-Supervised Lea...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Gao, Zhengqing Wu, Huimin Takac, Martin Gu, Bin Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Nanjing Univ Informat Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781450392365

semi-supervised support vector machine ((SVM)-V-3) is one of the most popular methods for semi-supervised learning, which can make full use of plentiful, easily accessible unlabeled data. Balancing constraint is normally enforced in (SVM)-V-3 (denoted as (BCSVM)-V-3) to avoid the harmful solution which assigns all or most of the unlabeled examples to one same label. Traditionally, non-linear (BCSVM)-V-3 is solved by sequential minimal optimization algorithm. Recently, a novel incremental learning algorithm (IL-(BCSVM)-V-3) was proposed to scale up (BCSVM)-V-3 further. However, IL-(BCSVM)-V-3 needs to calculate the inverse of the linear system related to the support matrix, making the algorithm not scalable enough. To make (BCSVM)-V-3 be more practical in large-scale problems, in this paper, we propose a new scalable (BCSVM)-V-3 with accelerated triply stochastic gradients (denoted as TSG-(BCSVM)-V-3). Specifically, to make the balancing constraint handle different proportions of positive and negative samples among labeled and unlabeled data, we propose a soft balancing constraint for S3VM. To make the algorithm scalable, we generate triply stochastic gradients by sampling labeled and unlabeled samples as well as the random features to update the solutions, where Quasi-Monte Carlo (QMC) sampling is utilized on random features to accelerate TSG-(BCSVM)-V-3 further. Our theoretical analysis shows that the convergence rate is O(1/root T) for both diminishing and constant learning rates where T is the number of iterations, which is much better than previous results thanks to the QMC method. Empirical results on a variety of benchmark datasets show that our algorithm not only has a good generalization performance but also enjoys better scalability than existing (BCSVM)-V-3 algorithms.

关键词： semi-supervised support vector machine balancing constraint

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semi-supervised Learning-enabled Two-stage Framework for Cooperative Spectrum Sensing Against SSDF Attack

Semi-supervised Learning-enabled Two-stage Framework for Coo...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Chen, Ze Wu, Jun Bao, Jianrong Hangzhou Dianzi Univ Sch Commun Engn Hangzhou Zhejiang Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Jiangsu Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin Sichuan Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665442664

Cooperative spectrum sensing (CSS) has been considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, spectrum sensing data falsification (SSDF) can hugely degrade the achievable detection accuracy of the primary user (PU) channel status in cognitive radio (CR) networks and thus requires learning from past CR network environments. Using the supervised learning (SL) method, a large number of labels about the real state of the channel availability need to be acquired. In a practical setting, however, obtaining off-the-shelf label data is a challenging undertaking because it necessitates cooperation among PUs and secondary users (SUs) and increases communication overhead in CR networks. Motivated by this, we solve the issue of label data scarcity in CR networks in this paper, and propose a semi-supervised learning-enabled two-stage framework for CSS against SSDF attack, in which combines the superior performance of semi-supervised support vector machine (S3VM) and the fast convergence of K-means. At last, simulation results demonstrate that S3VM is equipped with a greater detection performance than support vector data description (SVDD), and the same detection performance as support vector machine (SVM), especially it also the robustness and excellent performance using a small amount of labeled data.

关键词： Cooperative spectrum sensing spectrum sensing data falsification attack semi-supervised support vector machine K-means two-stage framework

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Least-Squares support vector machine for semi-supervised Multi-Tasking 16

Least-Squares Support Vector Machine for Semi-Supervised Mul...

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16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Jia, Xuekuo Wang, Shipu Yang, Yun Yunnan Univ Sch Software Kunming Yunnan Peoples R China

ISBN: (纸本)9781538658864

The semi-supervised multi-tasking using least-squares support vector machine can further improve performance by using related information of related tasks, and it inherits the advantages of high training speed and high efficiency of the least square support vector machine. Standard support vector machine is based on supervised learning, and it is necessary to manually mark large amounts of data for obtaining sufficient training data, which is costly and inefficient. In this paper, we apply least squares support vector machine based on semi-supervised learning to the multi-tasks and propose a semi-supervised multi-tasking approach using least-squares support vector machine. Based on related tasks learning simultaneously, multi-task least-squares support vector machine is used to train both labeled and unlabeled samples, overcoming the limitation of slow training, and using the useful information among related tasks to improve the efficiency of all tasks. In the training process, the regional tagging and the tag reset methods are used to reduce the number of iterations to achieve convergence and increases the fault tolerance rate. The experiment on the actual dataset shows the effectiveness of the approach.

关键词： multi-task learning least-squares support vector machine semi-supervised learning semi-supervised support vector machine semi-supervised multi-task learning

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Tackle Balancing Constraint for Incremental semi-supervised support vector Learning 19

Tackle Balancing Constraint for Incremental Semi-Supervised ...

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25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)

作者： Yu, Shuyang Gu, Bin Ning, Kunpeng Chen, Haiyan Pei, Jian Huang, Heng Northeastern Univ Boston MA 02115 USA JD Finance America Corp Mountain View CA USA Nanjing Univ Aeronaut & Astronaut Nanjing Jiangsu Peoples R China Simon Fraser Univ JD Com Burnaby BC Canada Univ Pittsburgh JD Finance Amer Corp Pittsburgh PA 15260 USA

ISBN: (纸本)9781450362016

semi-supervised support vector machine ((SVM)-V-3) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with (SVM)-V-3 (denoted as (BCSVM)-V-3). Recently, a novel incremental learning algorithm (IL-(SVM)-V-3) based on the path following technique was proposed to significantly scale up (SVM)-V-3. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling (BCSVM)-V-3. To fill this gap, in this paper, we propose a new incremental (SVM)-V-3 algorithm (IL-(BCSVM)-V-3) based on IL-(SVM)-V-3 which can effectively handle the balancing constraint and directly update the solution of (BCSVM)-V-3. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into (SVM)-V-3. More importantly, we provide the finite convergence analysis for our IL-(BCSVM)-V-3 algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-(BCSVM)-V-3, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.

关键词： semi-supervised support vector machine balancing constraint incremental learning path following algorithm

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A winch fault classification algorithm based on cluster kernel semi-supervised support vector machine

A winch fault classification algorithm based on cluster kern...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Shi, Xianxin Zhao, Zhongxiang Zhu, Changjian Kong, Xiaoxiao Chai, Junfei Li, Lijing Zhao, Huan Xuzhou Heavy Machinery CO LTD Xuzhou 221004 Jiangsu Peoples R China

ISBN: (纸本)9783037855034

A cluster kernel semi-supervised support vector machine ((CKSVM)-V-3) based on spectral cluster algorithm is proposed and applied in winch fault classification in this paper. The spectral clustering method is used to re-represent original data samples in an eigenvector space so as to make the data samples in the same cluster gather together much better. Then, a cluster kernel function is constructed upon the eigenvector space. Finally, a cluster kernel (SVM)-V-3 is designed which can satisfy the cluster assumption of semi-supervised study. The experiments on winch fault classification show that the novel approach has high classification accuracy.

关键词： spectral cluster algorithm cluster kernel function semi-supervised support vector machine fault classification

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