An integrated autoencoder-based filter for sparse big data

被引：3

作者：

Peng, Wei ^{[1
]}

Xin, Baogui ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Econ & Management, Qingdao, Peoples R China

来源：

JOURNAL OF CONTROL AND DECISION | 2021年 / 8卷 / 03期

关键词：

Sparse big data; integrated autoencoder (IAE); data sparsity; prediction; filter; STACKED AUTOENCODER; DEEP; PREDICTION; NETWORK; MODEL;

D O I：

10.1080/23307706.2020.1759466

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilises auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.

引用

页码：260 / 268

页数：9

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