Locally Linear Approximation Approach for Incomplete Data
Locally Linear Approximation Approach for Incomplete Data
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DOI：
10.1109/tcyb.2017.2713989
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发表时间：
2018-06
影响因子：
11.8
通讯作者：
Jianhua Dai;Hu Hu-Hu;Q. Hu;Wei Huang;Nenggan Zheng;Liang Liu
中科院分区：
文献类型：
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作者：
Jianhua Dai;Hu Hu-Hu;Q. Hu;Wei Huang;Nenggan Zheng;Liang Liu
矩阵完备化问题是指在处理不完整数据时，恢复一个给定的含有缺失元素的矩阵。在许多已有的研究中，秩最小化在矩阵完备化中起着核心作用。在本文中，注意到局部线性重建可以用来近似丢失的条目，我们从一个新的角度来看这个问题，并提出了一个算法称为局部线性近似（LLA）。LLA方法试图保持数据空间的局部结构，同时从行角度和列角度恢复丢失的条目。实验结果证明了该方法的有效性。
The matrix completion problem is restoring a given matrix with missing entries when handling incomplete data. In many existing researches, rank minimization plays a central role in matrix completion. In this paper, noticing that the locally linear reconstruction can be used to approximate the missing entries, we view the problem from a new perspective and propose an algorithm called locally linear approximation (LLA). The LLA method tries to keep the local structure of the data space while restoring the missing entries from row angle and column angle simultaneously. The experimental results have demonstrated the effectiveness of the proposed method.