A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
In the IoT and WSN era, large number of connected objects and sensing devices are
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …
Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Fast and accurate non-negative latent factor analysis of high-dimensional and sparse matrices in recommender systems
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 …
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
A survey of machine learning techniques for indoor localization and navigation systems
P Roy, C Chowdhury - Journal of Intelligent & Robotic Systems, 2021 - Springer
In the recent past, we have witnessed the adoption of different machine learning techniques
for indoor positioning applications using WiFi, Bluetooth and other technologies. The …
for indoor positioning applications using WiFi, Bluetooth and other technologies. The …
DNN-based indoor localization under limited dataset using GANs and semi-supervised learning
W Njima, A Bazzi, M Chafii - IEEE access, 2022 - ieeexplore.ieee.org
Indoor localization techniques based on supervised learning deliver great performance
accuracy while maintaining low online complexity. However, such systems require massive …
accuracy while maintaining low online complexity. However, such systems require massive …
An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems
Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …
Efficient and high-quality recommendations via momentum-incorporated parallel stochastic gradient descent-based learning
X Luo, W Qin, A Dong, K Sedraoui… - IEEE/CAA Journal of …, 2020 - ieeexplore.ieee.org
A recommender system (RS) relying on latent factor analysis usually adopts stochastic
gradient descent (SGD) as its learning algorithm. However, owing to its serial mechanism …
gradient descent (SGD) as its learning algorithm. However, owing to its serial mechanism …
A fast non-negative latent factor model based on generalized momentum method
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method
Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a
target matrix, which is critically important in collaborative filtering (CF)-based recommender …
target matrix, which is critically important in collaborative filtering (CF)-based recommender …
Generalized nesterov's acceleration-incorporated, non-negative and adaptive latent factor analysis
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 …
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …