Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDM.2013.83 | Data Mining |
Keywords | Field | DocType |
data analysis,matrix decomposition,tensors,auxiliary data tensors,data analysis,higher-order tensors,nonnegative multiple tensor factorization,target data tensor sparseness,Machine Learning,Non-negative Tensor Factorization,Spatio-Temporal Pattern | Tensor product network,Data set,Tensor,Computer science,Matrix decomposition,Stress (mechanics),Artificial intelligence,Probabilistic logic,Tensor factorization,Machine learning,Sparse matrix | Conference |
ISSN | Citations | PageRank |
1550-4786 | 18 | 0.72 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koh Takeuchi | 1 | 59 | 11.29 |
| Ryota Tomioka | 2 | 1367 | 91.68 |
| Ishiguro, K. | 3 | 25 | 1.20 |
| Kimura, A. | 4 | 21 | 1.55 |