A framework of uniform contribution embedding of data

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• 作者：Yuelong Li^a ; ^b ; ^c ; ¹ ; ^{liyuelong@pku.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{liyuelong@tjpu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yan Jin^aAuthor Vitae ; Jianming Wang^aAuthor Vitae ; Zhitao Xiao^dAuthor Vitae ; Lei Geng^dAuthor Vitae
• 关键词：Uniform contribution ; Multiple view embedding ; Information merging ; Data visualization ; Attribute dissimilarity
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：208
• 期：Complete
• 页码：193-201
• 全文大小：1986 K

文摘

In the scenario of big data, incorporating multiple attribute descriptions of the same subject (i.e., so-called multi-view data analysis) and the developing effective data visualization techniques have been one of the most important topics in the computer vision and machine learning community. In this paper, a multiple view source attributes embedding framework is proposed that can assemble various object attributes together and embed them into a low dimensional space to realize visual data structure demonstration. Classical sole attribute data embedding approaches such as MDS and SHE can be utilized under this framework. The core idea of this framework is to achieve a uniform contribution merging, which means every source attribute will contribute equally to the final embedded output. The underlying reason for this idea is that now that an attribute is input as a source and no prior weight is given beforehand, it should be treated equally. The most exciting advantage of this strategy is that it can avoid prejudice caused by major attributes (exerting due to their evident quantity advantage), which may lead the minor attributes be ignored. In addition, we propose a simple iterating algorithm to implement this framework, and plenty of experiments are conducted under diverse source attribute configurations to validate its effectiveness.