Non-negative Multiple Tensor Factorization
Non-negative Multiple Tensor Factorization
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DOI：
10.1109/icdm.2013.83
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Koh Takeuchi;Ryota Tomioka;Katsuhiko Ishiguro;Akisato Kimura;H. Sawada
中科院分区：
文献类型：
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作者：
Koh Takeuchi;Ryota Tomioka;Katsuhiko Ishiguro;Akisato Kimura;H. Sawada
非负张量因子分解（NTF）是一种广泛使用的技术，用于将非负值张量分解为稀疏且合理可解释的因子。然而，当张量非常稀疏时，NTF的性能很差，这通常是真实世界数据和高阶张量的情况。在本文中，我们提出了非负多重张量因子分解（NMTF），它同时分解目标张量和辅助张量。辅助数据张量补偿目标数据张量的稀疏性。辅助张量的因子也允许我们从几个不同的方面检查目标数据。我们实验证实，NMTF表现优于NTF重建给定的数据。此外，我们证明，建议NMTF可以成功地提取人们的日常生活，如休闲，饮酒，购物活动的时空模式，通过分析几个张量提取在线评论数据集。
Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In this paper, we propose Non-negative Multiple Tensor Factorization (NMTF), which factorizes the target tensor and auxiliary tensors simultaneously. Auxiliary data tensors compensate for the sparseness of the target data tensor. The factors of the auxiliary tensors also allow us to examine the target data from several different aspects. We experimentally confirm that NMTF performs better than NTF in terms of reconstructing the given data. Furthermore, we demonstrate that the proposed NMTF can successfully extract spatio-temporal patterns of people's daily life such as leisure, drinking, and shopping activity by analyzing several tensors extracted from online review data sets.