Neulft: A Novel Approach to Nonlinear Canonical Polyadic Decomposition on High-Dimensional Incomplete Tensors
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3176466
Abstract: A High-Dimensional and Incomplete (HDI) tensor is frequently encountered in a big data-related application concerning the complex dynamic interactions among numerous entities. Traditional tensor factorization-based models cannot handle an HDI tensor efficiently, while existing latent… read more here.
Keywords: tensor; hdi tensor; novel approach; dimensional incomplete ... See more keywords
A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Services Computing"
DOI: 10.1109/tsc.2023.3319713
Abstract: 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… read more here.
Keywords: based approach; dimensional incomplete; hdi; high dimensional ... See more keywords
MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Services Computing"
DOI: 10.1109/tsc.2023.3331570
Abstract: High-dimensional and incomplete (HDI) data are omnipresent in a variety of Big Data-related applications. Latent feature analysis (LFA) is a typical representation learning method that can extract useful yet latent knowledge from HDI data via… read more here.
Keywords: latent feature; dimensional incomplete; hdi; high dimensional ... See more keywords
Gaussian Graphical Model Estimation and Selection for High-Dimensional Incomplete Data Using Multiple Imputation and Horseshoe Estimators
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DOI: 10.3390/math12121837
Abstract: Gaussian graphical models have been widely used to measure the association networks for high-dimensional data; however, most existing methods assume fully observed data. In practice, missing values are inevitable in high-dimensional data and should be… read more here.
Keywords: incomplete data; gaussian graphical; dimensional incomplete; high dimensional ... See more keywords