Recent studies have focused on the use of tensor analysis for tensor decomposition because this method can identify more latent factor and patterns, compared to the matrix factorization approach. The existing tensor decomposition studies used static dataset in their analyses. However, in practice, data change and increase over time. Therefore, this paper proposes an incremental Parallel Factor Analysis (PARAFAC) tensor decomposition algorithm for three-dimensional tensors. The method of incremental tensor decomposition can reduce recalculation costs associated with the addition of new tensors. The proposed method is called InParTen; it performs distributed incremental PARAFAC tensor decomposition based on the Apache Spark framework. The proposed method decomposes only new tensors and then combines them with existing results without recalculating the complete tensors. In this study, it was assumed that the tensors grow with time as the majority of the dataset is added over a period. In this paper, the performance of InParTen was evaluated by comparing the obtained results for execution time and relative errors against existing tensor decomposition tools. Consequently, it has been observed that the method can reduce the recalculation cost of tensor decomposition.
|Title of host publication||Web Engineering - 19th International Conference, ICWE 2019, Proceedings|
|Editors||In-Young Ko, Maxim Bakaev, Flavius Frasincar|
|Number of pages||9|
|State||Published - 2019|
|Event||19th International Conference on Web Engineering, ICWE 2019 - Daejeon, Korea, Republic of|
Duration: 11 Jun 2019 → 14 Jun 2019
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||19th International Conference on Web Engineering, ICWE 2019|
|Country/Territory||Korea, Republic of|
|Period||11/06/19 → 14/06/19|
Bibliographical noteFunding Information:
Acknowledgement. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931529)
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931529).
© Springer Nature Switzerland AG 2019.
- Apache spark
- Incremental tensor decomposition
- PARAFAC decomposition