Journal ArticleDOI
A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle
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TLDR
A new method of data missing estimation with tensor heterogeneous ensemble learning based on FNN (Fuzzy Neural Network) named FNNTEL is proposed in this paper and the performance is better than other commonly used technologies and different missing data generation models.About:
This article is published in Neurocomputing.The article was published on 2021-01-08. It has received 102 citations till now. The article focuses on the topics: Missing data & Imputation (statistics).read more
Citations
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A survey on missing data in machine learning.
Tlamelo Emmanuel,Thabiso M. Maupong,Dimane Mpoeleng,Thabo Semong,Banyatsang Mphago,Oteng Tabona +5 more
TL;DR: This paper aggregates some of the literature on missing data particularly focusing on machine learning techniques, and gives insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for.
Journal ArticleDOI
Trust Mechanism of Feedback Trust Weight in Multimedia Network
LvZhihan,SongHoubing +1 more
TL;DR: This research aims to solve problems arising from the trust mechanism of multimedia and its mechanism and put forward a Feedback Trust Weighted for Data Fusion algorithm (FTWDF) drawing upon the co-operation of Facebook and Google.
Journal ArticleDOI
Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning
TL;DR: A hybrid algorithm to help intelligent driving vehicles selecting the best path in the traffic network in emergencies including limited height, width, weight, accident, and traffic jam is designed.
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A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm
TL;DR: This paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve the path planning problem of intelligent driving vehicles and designed new update rules for inertia weight and learning factors to overcome these problems.
Journal ArticleDOI
A new algorithm of clustering AODV based on edge computing strategy in IOV
TL;DR: In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed, considering the vehicle node energy and speed, the routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle and vehicle to road (V2R) mode.
References
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Journal ArticleDOI
Tensor Decompositions and Applications
Tamara G. Kolda,Brett W. Bader +1 more
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
Journal ArticleDOI
Tensor completion for estimating missing values in visual data
TL;DR: The contribution of this paper is to extend the matrix case to the tensor case by proposing the first definition of the trace norm for tensors and building a working algorithm to estimate missing values in tensors of visual data.
Posted Content
Tensor decompositions for learning latent variable models
TL;DR: A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin's perturbation theorem for the singular vectors of matrices, and implies a robust and computationally tractable estimation approach for several popular latent variable models.
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Tensor decompositions for learning latent variable models
TL;DR: In this article, the authors consider a wide class of latent variable models, including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation, which exploit a certain tensor structure in their low-order observable moments (typically, of second and third-order).
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Scalable tensor factorizations for incomplete data
TL;DR: An algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem and is shown to successfully factorize tensors with noise and up to 99% missing data.