Robust Simultaneous Low Rank Approximation of Tensors
Robust Simultaneous Low Rank Approximation of Tensors
复制标题
DOI：
10.1007/978-3-540-92957-4_50
复制
发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
K. Inoue;K. Hara;K. Urahama
中科院分区：
文献类型：
--
作者：
K. Inoue;K. Hara;K. Urahama
我们提出了张量的同时低秩近似（SLRAT）来降低张量的维数，并将其修改为鲁棒的SLRAT，即鲁棒SLRAT。对于SLRAT和鲁棒SLRAT，我们提出了求解它们的迭代算法。实验表明，当数据集包含噪声数据时，鲁棒SLRAT比SLRAT获得更低的重构误差。我们还提出了一种对张量集进行分类的方法，称为子空间匹配，其中训练数据和测试数据都由它们的子空间表示，每个测试数据根据子空间之间的相似性进行分类。实验证明，当测试数据中含有噪声数据时，鲁棒SLRAT比SLRAT具有更高的识别率。
We propose simultaneous low rank approximation of tensors (SLRAT) for the dimensionality reduction of tensors and modify it to the robust one, i.e., the robust SLRAT. For both the SLRAT and the robust SLRAT, we propose iterative algorithms for solving them. It is experimentally shown that the robust SLRAT achieves lower reconstruction error than the SLRAT when a dataset contains noise data. We also propose a method for classifying sets of tensors and call it the subspace matching, where both training data and testing data are represented by their subspaces, and each testing datum is classified on the basis of the similarity between subspaces. It is experimentally verified that the robust SLRAT achieves higher recognition rate than the SLRAT when the testing data contain noise data.