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distance metric learning guided adaptive subspace semi-supervised clustering

Frontiers of Computer Science 2011年第1期5卷 100-108页

作者： Xuesong Yin (12) yinxs@*** Enliang Hu (1) Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn Nanjing 210016 Peoples R China Zhejiang Radio & TV Univ Dept Comp Sci & Technol Hangzhou 310030 Zhejiang Peoples R China

Most existing semi-supervised clustering algorithms are not designed for handling high- dimensional data. On the other hand, semi-supervised dimensionality reduction methods may not necessarily improve the clustering performance, due to the fact that the inherent relationship between subspace selection and clustering is ignored. In order to mitigate the above problems, we present a semi-supervised clustering algo- rithm using adaptive distance metric learning （SCADM） which performs semi-supervised clustering and distance metric learning simultaneously. SCADM applies the clustering results to learn a distance metric and then projects the data onto a low-dimensional space where the separability of the data is maximized. Experimental results on real-world data sets show that the proposed method can effectively deal with high-dimensional data and provides an appealing clustering performance.

关键词： semi-supervise clustering pairwise con-straint distance metric learning data mining

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Data-driven Transient Stability Assessment Based on Kernel Regression and distance metric learning

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 27-36页

作者： Xianzhuang Liu Yong Min Lei Chen Xiaohua Zhang Changyou Feng the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China State Grid Jibei Electric Power Company Beijing 100053China State Grid Corporation of China Beijing 100031China

Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power *** can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training ***, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

关键词： Transient stability assessment(TSA) critical clearing time(CCT) conservativeness level distance metric learning Nadaraya-Watson kernel regression Mahalanobis distance nonparametric regression data-driven

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Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model

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Frontiers of Information Technology & Electronic Engineering 2019年第9期20卷 1195-1208页

作者： Ya XIAO Zhi-jie FAN Amiya NAYAK Cheng-xiang TAN College of Electronics and Information Engineering Tongji UniversityShanghai 201804China School of Electrical Engineering and Computer Science University of OttawaOttawa K1N 6N5Canada The Third Research Institute of the Ministry of Public Security Shanghai 200120China

The security threats to software-defined networks(SDNs)have become a significant problem,generally because of the open framework of *** all the threats,distributed denial-of-service(DDoS)attacks can have a devastating impact on the *** propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph *** feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links;it can demonstrate not only the traffc header information but also the relationships among all the network *** similarity between nodes is modeled by metric learning and the Mahalanobis *** proposed method can discover DDoS attacks using a graph-based neighborhood classification method;it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global *** on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks,and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.

关键词： Software-defined network Distributed denial-of-service(DDoS) Behavior discovery distance metric learning Feature-pattern graph

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Image Annotation with Nearest Neighbor Based on Semantic Information

Image Annotation with Nearest Neighbor Based on Semantic Inf...

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2015年中国智能自动化学术会议

作者： Wei Wu Guanglai Gao Department of Computer Science Inner Mongolia University

Most of the Nearest Neighbor(NN)-based image annotation methods do not achieve desired performances. The main reason is that much valuable information is lost when extracting visual features from image. In this paper, we propose a novel weighted NN-based method. Instead of using Euclidean distance, we learn a new distance metric with image semantic information to calculate the distance between the two images. Meanwhile, we utilize textual information of each image tagged by users to form weights of NN-based model. When introducing the semantic information, our method can minimize the semantic gap for intraclass variations and interclass similarities, and improve the annotation *** on image annotation dataset of Image CLEF2012 show that our method outperforms the traditional classifiers. Moreover, our method is simple,efficient, and competitive compared with state-of-the-art learning-based models.

关键词： Image annotation Nearest neighbor distance metric learning Entropy weight

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Gaussian Mixture Model with Semantic distance for Image Classification

Gaussian Mixture Model with Semantic Distance for Image Clas...

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第26届中国控制与决策会议

作者： Wei Wu Guanglai Gao Jianyun Nie College of Computer Science Inner Mongolia University Department of Computer Science and Operations Research University of Montreal

ISBN: (纸本)9781479937097

This paper mainly introduces a new method for image classification. The traditional Bag of Visual Words model（BoVW） is a promising image representation technique for image classification. But its limitation is that much valuable information is lost when building the codebook of BoVW simply by clustering visual features in the Euclidian space. In this paper, we take full advantage of image semantic information to learn a new distance metric which achieving the minimal loss of image information, and then we learn visual words by clustering the local features using Gaussian Mixture Models（GMMs） with this distance metric. When given a test image, it firstly forms a visual document using GMM based on our learned distance metric, then its category is determined by estimating the maximum probability using language model under a specific category. Experimental results confirm the effectiveness of our method, results are satisfactory and competitive compared with traditional and state of the art methods.

关键词： Bag of Visual Words Image Classification GMM distance metric learning Language Model

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