A Low Tensor-Rank Representation Approach for Clustering of Imaging Data
A Low Tensor-Rank Representation Approach for Clustering of Imaging Data
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DOI：
10.1109/lsp.2018.2849590
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发表时间：
2018-06
影响因子：
3.9
通讯作者：
Tong Wu;W. Bajwa
中科院分区：
文献类型：
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作者：
Tong Wu;W. Bajwa
这封信提出了一种二维数据聚类算法。所提出的算法不是将数据“展平”为向量，而是将样本保留为矩阵并将它们存储为三阶张量中的横向切片。然后假设样本位于自由子模块的并集附近，并且它们在该模型下的表示是通过对表示张量施加低张量秩约束和结构约束来获得的。使用根据表示张量计算的亲和力矩阵进行聚类。通过对两个图像数据集的实验证明了所提出算法的有效性及其相对于现有方法的优越性。
This letter proposes an algorithm for clustering of two-dimensional data. Instead of “flattening” data into vectors, the proposed algorithm keeps samples as matrices and stores them as lateral slices in a third-order tensor. It is then assumed that the samples lie near a union of free submodules and their representations under this model are obtained by imposing a low tensor-rank constraint and a structural constraint on the representation tensor. Clustering is carried out using an affinity matrix calculated from the representation tensor. Effectiveness of the proposed algorithm and its superiority over existing methods are demonstrated through experiments on two image datasets.