Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第2页

Online structural SVM learning by dual ascending procedure 基于对偶上升过程的在线结构支持向量机学习

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982687

Jun Lei, Guohui Li, Jun Zhang, Dan Lu, Qiang Guo

引用次数: 0

A candidate solutions generator based on mixed strategy for non-rigid object extraction 一种基于混合策略的非刚体对象提取候选解生成器

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982712

Min Jiang, Xiaozhou Zhou, Shijie Yao, Zhaohui Gan

引用次数: 0

An adaptive computational method for color contrast based salient region detection 一种基于颜色对比度的显著区域检测自适应计算方法

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982656

Xin Xu, Weiwei Wu

引用次数: 0

Overlapping community detection via link partition of asymmetric weighted graph 基于非对称加权图链路划分的重叠社团检测

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982726

Wenju Zhang, Naiyang Guan, Xuhui Huang, Zhigang Luo, Jianwu Li

{"title":"Overlapping community detection via link partition of asymmetric weighted graph","authors":"Wenju Zhang, Naiyang Guan, Xuhui Huang, Zhigang Luo, Jianwu Li","doi":"10.1109/SPAC.2014.6982726","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982726","url":null,"abstract":"Link partition clusters edges of a complex network to discover its overlapping communities. Due to Its effectiveness, link partition has attracted much attentions from the network science community. However, since link partition assigns each edge of a network to unique community, it cannot detect the disjoint communities. To overcome this deficiency, this paper proposes a link partition on asymmetric weighted graph (LPAWG) method for detecting overlapping communities. Particularly, LPAWG divides each edge into two parts to distinguish the roles of connected nodes. This strategy biases edges to a specific node and helps assigning each node to its affiliated community. Since LPAWG introduces more edges than those in the original network, it cannot efficiently detect communities from some networks with relative large amount of edges. We therefore aggregate the line graph of LPAWG to shrink its scale. Experimental results of community detection on both synthetic datasets and the realworld networks show the effectiveness of LPAWG comparing with the representative methods.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128067575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Image scaling factor estimation based on normalized energy density and learning to rank 基于归一化能量密度和学习排序的图像比例因子估计

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982684

Nan Zhu, Xinbo Gao, Cheng Deng

引用次数: 3

Multi-view embedding learning via robust joint nonnegative matrix factorization 基于鲁棒联合非负矩阵分解的多视图嵌入学习

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982680

Weihua Ou, Kesheng Zhang, Xinge You, Fei Long

{"title":"Multi-view embedding learning via robust joint nonnegative matrix factorization","authors":"Weihua Ou, Kesheng Zhang, Xinge You, Fei Long","doi":"10.1109/SPAC.2014.6982680","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982680","url":null,"abstract":"Real data often are comprised of multiple modalities or different views, which provide complementary and consensus information to each other. Exploring those information is important for the multi-view data clustering and classification. Multiview embedding is an effective method for multiple view data which uncovers the common latent structure shared by different views. Previous studies assumed that each view is clean, or at least there are not contaminated by noises. However, in real tasks, it is often that every view might be suffered from noises or even some views are partially missing, which renders the traditional multi-view embedding algorithm fail to those cases. In this paper, we propose a novel multi-view embedding algorithm via robust joint nonnegative matrix factorization. We utilize the correntropy induced metric to measure the reconstruction error for each view, which are robust to the noises by assigning different weight for different entries. In order to uncover the common subspace shared by different views, we define a consensus matrix subspace to constrain the disagreement of different views. For the non-convex objective function, we formulate it into half quadratic minimization and solve it via update scheme efficiently. The experiments results show its effectiveness and robustness in multiview clustering.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126043224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ultra local binary pattern for image texture analysis 图像纹理分析的超局部二值模式

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982701

Yiu-ming Cheung, Junping Deng

引用次数: 10

Robust weighted coarse-to-fine sparse tracking 鲁棒加权粗到细稀疏跟踪

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982648

Boxuan Zhong, Zijing Chen, Xinge You, Luoqing Li, Y. Xie, Shujian Yu

引用次数: 1

Laplacian regularized active learning for image segmentation 用于图像分割的拉普拉斯正则化主动学习

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982692

Lianbo Zhang, Dapeng Tao, Weifeng Liu

引用次数: 1

Co-regularization for classification 分类的协正则化

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982688

Yang Li, Dapeng Tao, Weifeng Liu, Yanjiang Wang

{"title":"Co-regularization for classification","authors":"Yang Li, Dapeng Tao, Weifeng Liu, Yanjiang Wang","doi":"10.1109/SPAC.2014.6982688","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982688","url":null,"abstract":"Semi-supervised learning algorithms that combine labeled and unlabeled data receive significant interests in recent years and are successfully deployed in many practical data mining applications. Manifold regularization, one of the most representative works, tries to explore the geometry of the intrinsic data probability distribution by penalizing the classification function along the implicit manifold. Although existing manifold regularization, including Laplacian regularization (LR) and Hessian regularization (HR), yields significant benefits for partially labeled classification, it is observed that LR suffers from the poor generalization and HR exhibits the characteristic of instability, both manifold regularization could not accurately reflect the ground-truth. To remedy the problems in single manifold regularization and approximate the intrinsic manifold, we propose Manifold Regularized Co-Training (Co-Re) framework, which combines the manifold regularization (LR and HR) and the algorithm co-training. Extensive experiments on the USAA video dataset are conducted and validate the effectiveness of Co-Re by comparing it with baseline manifold regularization algorithms.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131555671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1