A framework of uniform contribution embedding of data
A framework of uniform contribution embedding of data
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DOI：
10.1016/j.neucom.2015.12.121
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发表时间：
2016-10
期刊：
影响因子：
6
通讯作者：
Yuelong Li;Yan Jin;Jianming Wang;Zhitao Xiao;Lei Geng
中科院分区：
文献类型：
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作者：
Yuelong Li;Yan Jin;Jianming Wang;Zhitao Xiao;Lei Geng
用户提供的标签虽然在图像注释中发挥着重要作用，但也可能会抑制准确的注释，因为它们可能不完整。为了解决这个问题，本文提出了一种新的标签补全方法。为了利用尽可能多的信息，该方法设计具有以下特点：（1）低秩和误差稀疏性：将初始标签矩阵D分解为完整标签矩阵A和稀疏误差矩阵E，其中A进一步分解为基矩阵U和稀疏系数矩阵V，即D=UV+E。其中K⪡M，可以通过子空间构造实现相关标签和相似样本之间的信息共享。（2）局部重建结构一致性：在低维特征子空间和标签子空间中都保留了原始特征和标签空间。(3)促进基多样性：最小化U的列之间的成对点积，以获得更具代表性的基向量。在Corel5K数据集和新发布的Flickr30Concepts数据集上进行的实验证明了该方法的有效性和效率。
User provided tags, albeit play an essential role in image annotation, may inhibit accurate annotation as well since they are potentially incomplete. To address this problem, a novel tag completion method is proposed in this paper. In order to exploit as much information, the proposed method is designed with the following features:(1) Low-rank and error sparsity: the initial tag matrix D is decomposed into the complete tag matrix A and a sparse error matrix E, where A is further factorized into a basis matrix U and a sparse coefficient matrix V, ie, D= UV+ E. With K⪡ M, information sharing between related tags and similar samples can be achieved via subspace construction.(2) Local reconstruction structure consistency: the local linear reconstruction structures obtained in the original feature and tag spaces are preserved in both the low-dimensional feature subspace and tag subspace.(3) Promote basis diversity: the pair-wise dot products between the columns of U are minimized, in order to obtain more representative basis vectors. Experiments conducted on Corel5K dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method.