Abstract
For mass or temporal data, it is too large and even impossible for the calculated amount of dimension reduction all at once. Based on text feature graph clusters, first, each text feature graph serves as a second-order tensor. Then, two or more text feature graphs were made up to form a third-order tensor. Moreover, tensor Tucker decomposition is used to study the incremental dimensionality reduction methods of text feature graphs. Finally, experiments on real data sets show that this method is simple and effective for dimensionality reduction of text feature graphs.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (71171148, 61403238), the National Key Technology R&D Program (2012BAD35B01, 2012BAH13F04) and the National High-Tech Research and Development plan of China (2012AA062203), the National Basic Research Program of China (2014CB340404), and the Natural Science Foundation of Shanxi (2014021022-1).
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Guo, X. et al. (2016). Research of Incremental Dimensionality Reduction Based on Tensor Decomposition Algorithm. In: Zeng, QA. (eds) Wireless Communications, Networking and Applications. Lecture Notes in Electrical Engineering, vol 348. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2580-5_9
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DOI: https://doi.org/10.1007/978-81-322-2580-5_9
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