Abstract

Research on bispectrum analysis of secondary feature for vehicle exterior noise based on nonnegative tucker3 decomposition

Highlights

  • Nonnegative Tucker3 decomposition (NTD), known as a generalized form of nonnegative tensor factorization (NTF) model, has received hot attention by a considerable amount of people for its overwhelming performance of decomposition efficiency on multi-way dataset decomposition [1]. Both nonnegative Tucker3 decomposition (NTD) and NTF here can be viewed as high-order extensions of non-negative matrix factorization (NMF) method as referred in some literatures about relationship between NTF/NTD and NMF, all of whose factors are based on an alternating minimization of cost functions incorporating distances or divergences measures with its application in environmental data analysis and can be found in reference therein [2]

  • Just one of crucial usages of NTD is of feature extraction from high-order datasets ranging from signals, images, speech, neuroscience, systems biology, chemometrics, or texts [4,5,6,7,8,9,10], and used for designing complex systems as it is the case of wireless communication systems as the publication of the novel paper [11]

  • Overfitting appearing in the iterative procedure would generate a large number of harmonic waves as interrupting signal before feature extraction [12], which brings in hard difficulty in detecting the useful features for fault diagnosis and usually occurs in the frequency analysis of vehicle exte

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Summary

Introduction

Nonnegative Tucker decomposition (NTD), known as a generalized form of nonnegative tensor factorization (NTF) model, has received hot attention by a considerable amount of people for its overwhelming performance of decomposition efficiency on multi-way dataset decomposition [1]. NGGD can be developed as a way of updating the factors all-atonce as well This way will be used to reduce the complexity of iterative calculation, but be an available solution to the robustness of NTD and more significant to the matrices and core tensors, which are crucial to the basis images for reconstructing the secondary feature to analyze the vehicle exterior noise of an automobile car

Definition and notation
Updating algorithm of iterative calculation
Updating algorithm based on NGGD
Operator optimization
Bispectrum analysis of automobile vehicle exterior noise
Secondary feature extraction
Methods
Method
Results and discussion
Conclusions
Full Text
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