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4th international Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. Therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Unsupervised Nonlinear Adaptive Manifold learning for Global and Local Information

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Tsinghua Science and Technology 2021年第2期26卷 163-171页

作者： Jiajun Gao Fanzhang Li Bangjun Wang Helan Liang School of Computer Science and Technology Soochow UniversitySuzhou 215006China Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhou 215006China

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold learning method(UNAML)that considers both global and local *** this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology *** method aims at minimizing global pairwise data distance errors as well as local structural *** order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear ***,we add a feature approximate error that can be used to learn a linear *** addition,we use a method of adaptive neighbor selection to calculate local structural *** paper uses the kernel matrix method to optimize the original *** algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

关键词： unsupervised manifold learning global and local information,adaptive neighbor selection method kernel matrix

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Laplacain Pair-Weight Vector Projection with Adaptive Neighbor Graph for Semi-supervised learning 1

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3rd international Conference on Neural computing for Advanced Applications, NCAA 2022

作者： Xue, Yangtao Zhang, Li The School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

Recently, Laplacian pair-weight vector projection (LapPVP) algorithm was proposed for semi-supervised classification. Although LapPVP achieves a good classification performance for semi-supervised learning, it may be sensitive to noise and outliers for using the neighbor graph with a fixed similarity matrix. To remedy it, this paper proposes a novel method named Laplacain pair-weight vector projection with adaptive neighbor graph (ANG-LapPVP), in which the graph induced by the Laplacian manifold regularization is adaptively constructed by solving an optimization problem. For binary classification problems, ANG-LapPVP learns a pair of projection vectors by solving the pair-wise optimal formulations in which we maximize the between-class scatter and minimize both the within-class scatter and the adaptive neighbor graph (ANG) regularization. The ANG regularization is to learn the ANG whose similarity matrix varies with iterations, which may solve the issue of LapPVP. Thus, ANG-LapPVP simultaneously learns adaptive similarity matrices and a pair of projection vectors with an iterative process. Experimental results on an artificial and real-world benchmark datasets show the superiority of ANG-LapPVP compared to the related methods. Thus, ANG-LapPVP is promising in semi-supervised learning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Unsupervised ensemble learning based on graph embedding for image clustering 25th

Unsupervised ensemble learning based on graph embedding for ...

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25th international Conference on Neural Information Processing, ICONIP 2018

作者： Luo, Xiaohui Zhang, Li Li, Fanzhang Hu, Chengxiang School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (纸本)9783030041816

Manifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the K-means method to obtain the clustering results. Furthermore, the multiple clusterings are corrected by using the bestMap method. Finally, the corrected clusterings are combined to generate the final clustering. Extensive experiments on several data sets are conducted to show the efficiency and effectiveness of the proposed ensemble learning method. © Springer Nature Switzerland AG 2018.

关键词： K-means clustering

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T CNN: a novel method for crowd counting via two-task convolutional neural network

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The Visual Computer 2021年第1期39卷 73-85页

作者： Zhang, Li Yan, Leilei Zhang, Mengqian Lu, Jingang Laboratory of Machine Learning and Neuromorphic Computing School of Computer Science and Technology and Joint International Research Soochow University Suzhou China

This paper investigates the issue of crowd counting for crowd images. A novel method named two-task convolutional neural network (T $$^2$$ CNN) is proposed to simultaneously learn two tasks for crowd counting: the tasks of dense degree classification and density map estimation. Basically, T $$^2$$ CNN contains two main modules: dense degree classification (DDC) and density map estimation. Generally, it is sufficient for crowd counting to estimate density maps of images. It is known that images may have different dense degrees and the local regions in the same image have different dense degrees. Thus, dense degree may provide the high-level prior of images and is excepted to help the estimation of density maps. To better estimate density maps of images, T $$^2$$ CNN incorporates the DDC module into the task of density map estimation. T $$^2$$ CNN takes the scale-adaptive convolutional neural network as the estimator of density maps, which adopts multi-scale layers to tackle the scale and perspective variations that exist in crowd images commonly. To verify the effectiveness and robustness of our model, we conduct extensive experiments on the Shanghaitech, UCF_CC_50 and WorldExpo’10 datasets. Experimental results indicate that T $$^2$$ CNN achieves good performance.

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Discriminative sparse coding by nuclear norm-driven semi-supervised dictionary learning 17th

Discriminative sparse coding by nuclear norm-driven semi-sup...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Jiang, Weiming Zhang, Zhao Zhang, Yan Li, Fanzhang School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China

ISBN: (纸本)9783319488899

In this paper, we propose a Nuclear norm-driven Semi-Supervised Dictionary learning (N-SSDL) approach for classification. N-SSDL incorporates the idea of the recent label consistent KSVD with the label propagation process that propagates label information from labeled data to unlabeled data via balancing the neighborhood reconstruction error and the label fitness error. To provide a more reliable distance metric for measuring the neighborhood reconstruction error, we apply the nuclear-norm that is proved to be suitable for modeling the reconstruction error, where the reconstruction coefficients are computed based on the sparsely reconstructed training data rather than original ones. Besides, we also use the robust l2,1-norm regularization on the label fitness error so that the measurement is robust to noise and outliers. Extensive simulations on several datasets show that N-SSDL can deliver enhanced performance over other state-of-the-arts for classification. © Springer international Publishing AG 2016.

关键词： Classification (of information)

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Transductive classification by robust linear neighborhood propagation 17th

Transductive classification by robust linear neighborhood pr...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Jia, Lei Zhang, Zhao Jiang, Weiming School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China

ISBN: (纸本)9783319488899

We propose an enhanced label prediction method termed Transductive Classification Robust Linear Neighborhood Propagation (R-LNP). To encode the neighborhood reconstruction error more accurately, we apply the L2,1-norm that is proved to be very robust to noise for characterizing the manifold smoothing term. Since L2,1-norm can also enforce the neighborhood reconstruction error to be sparse in rows, i.e., entries of some rows are zeros. In addition, to enhance robustness in the process of modeling the difference between the initial labels and predicted ones, we also regularize the weighted L2,1-norm on the label fitting term, so the resulted measures would be more accurate. Compared with several transductive label propagation models, our proposed algorithm obtains state-of-the-art performance over extensive representation and classification experiments. © Springer international Publishing AG 2016.

关键词： Artificial intelligence

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Sample Reduction Using 1 -Norm Twin Bounded Support Vector machine 2nd

Sample Reduction Using 1 -Norm Twin Bounded Support Vector M...

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2nd international Conference on Neural computing for Advanced Applications, NCAA 2021

作者： Zheng, Xiaohan Zhang, Li Yan, Leilei School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9789811651878

Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In order to improve the ability of TSVM to reduce sample, we propose an 1 -norm twin bounded support machine (1 -TBSVM) inspired by the sparsity of 1 -norm in feature space. The objective function of 1 -TBSVM contains the hinge loss and the 1 -norm terms, both which can induce sparsity. We solve the primal programming problems of 1 -TBSVM to prevent the disappearance of sparsity and avoid the situation that the inverse of matrix does not exist. Thus, 1 -TBSVM has a good sparsity, or a good ability to reduce sample. Experimental results on synthetic and UCI datasets indicate that 1 -TBSVM has a good ability to perform sample reduction and simultaneously enhances the classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Semi-supervised feature selection using sparse laplacian support vector machine 1

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1st international Conference on Neural computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Zheng, Xiaohan Xu, Zhiqiang School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

Semi-supervised feature selection is an active topic in machine learning and data mining. Laplacian support vector machine (LapSVM) has been successfully applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the 1-norm regularization, which means the solution of SLapSVM has sparsity. In addition, the training procedure of SLapSVM can be formulated as solving a quadratic programming problem, which indicates that the solution of SLapSVM is unique and global. SLapSVM can perform feature selection and classification at the same time. Experimental results on semi-supervised classification problems show the feasibility and effectiveness of the proposed semi-supervised learning algorithms. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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Reliable neighbors-based collaborative filtering for recommendation systems 1

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1st international Conference on Neural computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collaborative filtering

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