Multi-view hypergraph learning by patch alignment framework.pdfVIP

下载本文档

27
0
约 8页
2017-03-16 发布于天津
举报
版权申诉

Multi-view hypergraph learning by patch alignment framework.pdf

1、有哪些信誉好的足球投注网站（book118）网站文档一经付费（服务费），不意味着购买了该文档的版权，仅供个人/单位学习、研究之用，不得用于商业用途，未经授权，严禁复制、发行、汇编、翻译或者网络传播等，侵权必究。。
2、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。如您付费，意味着您自己接受本站规则且自行承担风险，本站不退款、不进行额外附加服务；查看《如何避免下载的几个坑》。如果您已付费下载过本站文档，您可以点击这里二次下载。
3、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等，请点击“版权申诉”（推荐），也可以打举报电话：400-050-0827(电话支持时间：9:00-18:30)。
4、该文档为VIP文档，如果想要下载，成为VIP会员后，下载免费。
5、成为VIP后，下载本文档将扣除1次下载权益。下载后，不支持退款、换文档。如有疑问请联系我们。
6、成为VIP后，您将拥有八大权益，权益包括：VIP文档下载权益、阅读免打扰、文档格式转换、高级专利检索、专属身份标志、高级客服、多端互通、版权登记。
7、VIP文档为合作方或网友上传，每下载1次，网站将根据用户上传文档的质量评分、类型等，对文档贡献者给予高额补贴、流量扶持。如果你也想贡献VIP文档。上传文档

Multi-view hypergraph learning by patch alignment framework.pdf

Neurocomputing 118 (2013) 79–86 Contents lists available at SciVerse ScienceDirect Neurocomputing journal homepage: /locate/neucom Multi-view hypergraph learning by patch alignment framework Chaoqun Hong a, Jun Yu b,c,n, Jonathan Li b,c, Xuhui Chen a a Faculty of Computer Science, Xiamen University of Technology, Xiamen, Fujian 361024, China b Department of Computer Science, Xiamen University, Xiamen, Fujian 361005, China c Key Laboratory for Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen University, Xiamen, Fujian 361005, China article info Article history: Received 29 September 2012 Received in revised form 4 January 2013 Accepted 5 February 2013 Communicated by M. Wang Available online 28 February 2013 Keywords: Dimensionality reduction Hypergraph Multi-view learning Patch alignment framework abstract Graph-based methods are currently popular for dimensionality reduction. However, most of them suffer from over-simpli?ed assumption of pairwise relationships among data. Especially for multi-view data, different relationships from different views are hard to be integrated into a single graph. In this paper, we propose a novel semi-supervised dimensionality reduction method for multi-view data. First, we assume the hyperedges in hypergraph as patches and apply hypergraph to the patch alignment framework. Second, the weights of the hyperedges are computed with statistics of distances between neighboring pairs and the patches from different views are integrated. In this way, we construct Multiview Hypergraph Laplacian matrix and we get the dimensionality-reduced data by solving the standard eigen-decomposition to obtain the projection matrix. The experimental results demonstrate the effectiveness of the proposed method on retrieval performance. 2013 Elsevier B.V. All rights reserved. 1. Introduction Images or objects could be represented by several types of features in the related researches on co