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CSpace
A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data
Bi, Fanghui1,2; He, Tiantian3; Luo, Xin4
2024-05-01
摘要High-Dimensional and Incomplete (HDI) data are frequently encountered in various Big Data-related applications. Despite its incompleteness, an HDI data repository contains rich knowledge and patterns concerning the complex interactions among numerous nodes. Recently, a Neural Network (NN)-based approach to Latent Feature Analysis (LFA) model becomes popular owing to its strong representation learning ability to HDI data. Nevertheless, existing NN-based LFA models neglect the inherent nonnegativity in most HDI data, resulting in representation accuracy loss. Motivated by this discovery, this study innovatively proposes a Fast Nonnegative Auto Encoder (FNAE)-based approach to LFA on HDI data, whose ideas are three-fold: a) constructing a multilayered autoencoder subject to nonnegativity constraints for high representation learning ability; b) incorporating the data density-oriented modeling mechanism into FNAE's input and output layers for high computational and storage efficiency; and c) implementing an Adam-based single latent factor-dependent, nonnegative and multiplicative update algorithm for efficient model training as well as fulfilling the nonnegativity constraints. Experimental results on eight commonly-adopted HDI matrices from industrial applications demonstrate that the proposed FNAE significantly outperforms several state-of-the-art NN-based LFA models in both estimation accuracy for missing links of an HDI matrix and computational efficiency.
关键词Knowledge acquisition data science high-dimensional and incomplete data neural network fast nonnegative AutoEncoder latent feature analysis link prediction network representation learning
DOI10.1109/TSC.2023.3319713
发表期刊IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN1939-1374
卷号17期号:3页码:733-746
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:001248286200008
语种英语