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   Articles with "latent factor" as a keyword



Spatial Comprehensive Well-Being Composite Indicators Based on Bayesian Latent Factor Model: Evidence from Italian Provinces

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recommendations! Published in 2024 at "Social Indicators Research"

DOI: 10.1007/s11205-023-03285-5

Abstract: This paper proposes spatial comprehensive composite indicators to evaluate the well-being levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being dashboard. We use a method based on Bayesian latent factor… read more here.

Keywords: latent factor; italian provinces; spatial comprehensive; composite indicators ... See more keywords
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Latent Factor Modeling of scRNA-Seq Data Uncovers Dysregulated Pathways in Autoimmune Disease Patients

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recommendations! Published in 2020 at "iScience"

DOI: 10.1016/j.isci.2020.101451

Abstract: Summary Latent factor modeling applied to single-cell RNA sequencing (scRNA-seq) data is a useful approach to discover gene signatures. However, it is often unclear what methods are best suited for specific tasks and how latent… read more here.

Keywords: scrna seq; factor modeling; latent factor; seq data ... See more keywords

Elastic-net regularized latent factor analysis-based models for recommender systems

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recommendations! Published in 2019 at "Neurocomputing"

DOI: 10.1016/j.neucom.2018.10.046

Abstract: Abstract Latent factor analysis (LFA)-based models are highly efficient in recommender systems. The problem of LFA is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is… read more here.

Keywords: latent factor; factor analysis; based models; factor ... See more keywords

Examining the latent factor structure of a hypothetical cigarette purchase task among pregnant women.

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recommendations! Published in 2022 at "Experimental and clinical psychopharmacology"

DOI: 10.1037/pha0000571

Abstract: The cigarette purchase task (CPT) is a valid behavioral-economic measure of demand that has smokers estimate hypothetical cigarette consumption under a range of escalating prices. The task involves no experimenter exposure of participants to smoking.… read more here.

Keywords: task; cigarette; factor; pregnant women ... See more keywords

A Proportional Integral Controller-Enhanced Non-Negative Latent Factor Analysis Model

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recommendations! Published in 2025 at "IEEE/CAA Journal of Automatica Sinica"

DOI: 10.1109/jas.2024.125055

Abstract: A non-negative latent factor (NLF) model is able to be built efficiently via a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm for performing precise representation to high-dimensional and incomplete (HDI) matrix from many… read more here.

Keywords: non negative; proportional integral; model; nlf model ... See more keywords

A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis

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recommendations! Published in 2024 at "IEEE Transactions on Automation Science and Engineering"

DOI: 10.1109/tase.2023.3284819

Abstract: High-dimensional and incomplete (HDI) data are commonly encountered in various big data-related applications concerning the complex interactions among numerous nodes, such as the user-item iterations in a recommender system. A stochastic gradient descent (SGD)-based latent… read more here.

Keywords: stochastic gradient; analysis; gradient; nonlinear pid ... See more keywords

A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis

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recommendations! Published in 2024 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2024.3389733

Abstract: A stochastic gradient descent (SGD) based latent factor analysis (LFA) model can obtain superior performance when performing representation to a high-dimensional and incomplete (HDI) matrix, which is encountered in various big data-related applications cause of… read more here.

Keywords: factor analysis; stochastic gradient; gradient descent; latent factor ... See more keywords

Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems

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recommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2021.3125252

Abstract: A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and Momentum-incorporated Update (SLF-NM2U) algorithm, which enables its fast convergence. It is crucial to… read more here.

Keywords: non negative; analysis; high dimensional; latent factor ... See more keywords

A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation

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recommendations! Published in 2023 at "IEEE Transactions on Network Science and Engineering"

DOI: 10.1109/tnse.2022.3206802

Abstract: Precise representation to undirected weighted network (UWN) is the foundation of understanding connection patterns inside a massive node set. It can be addressed via a Symmetric Non-negative Latent Factor (SNLF) model with a non-convex learning… read more here.

Keywords: order; model; second order; latent factor ... See more keywords

Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis

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recommendations! Published in 2022 at "IEEE Transactions on Services Computing"

DOI: 10.1109/tsc.2021.3069108

Abstract: A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising… read more here.

Keywords: underline underline; non negative; nesterov acceleration; latent factor ... See more keywords

A Momentum-Accelerated Hessian-Vector-Based Latent Factor Analysis Model

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recommendations! Published in 2023 at "IEEE Transactions on Services Computing"

DOI: 10.1109/tsc.2022.3177316

Abstract: Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions… read more here.

Keywords: hessian vector; lfa; model; latent factor ... See more keywords