Tensor completion via functional smooth component deflation
Tensor completion via functional smooth component deflation
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DOI：
10.1109/icassp.2016.7472130
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Tatsuya Yokota;A. Cichocki
中科院分区：
文献类型：
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作者：
Tatsuya Yokota;A. Cichocki
对于缺失率非常高的矩阵/张量补全问题，标准的基于局部（例如，补丁、概率和平滑）和全局（例如，低秩）结构的方法效果不佳。为了解决这个问题，我们建议通过应用一种新颖的函数平滑 PARAFAC 分解模型来同时使用局部和全局数据结构来完成张量。该分解模型被构造为函数平滑分量向量的外积之和，这些向量由平滑基函数的线性组合表示。通过应用贪婪紧缩和平滑的一阶张量分解开发了一种新算法。我们广泛的实验证明了我们的算法与现有最先进方法相比的高性能和优势。
For the matrix/tensor completion problem with very high missing ratio, the standard local (e.g., patch, probabilistic, and smoothness) and global (e.g., low-rank) structure-based methods do not work well. To address this issue, we proposed to use local and global data structures at the same time by applying a novel functional smooth PARAFAC decomposition model for the tensor completion. This decomposition model is constructed as a sum of the outer product of functional smooth component vectors, which are represented by linear combinations of smooth basis functions. A new algorithm was developed by applying greedy deflation and smooth rank-one tensor decomposition. Our extensive experiments demonstrated the high performance and advantages of our algorithm in comparison to existing state-of-the-art methods.