Automated detection of Parkinson's disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural …
Objective: Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported
that most of PD patients have voice impairments. But these voice impairments are not …
that most of PD patients have voice impairments. But these voice impairments are not …
Early diagnosis of Parkinson's disease from multiple voice recordings by simultaneous sample and feature selection
Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that more
than 90% of PD patients have voice impairments. Multiple types of voice recordings have …
than 90% of PD patients have voice impairments. Multiple types of voice recordings have …
[HTML][HTML] Generalized tensor function via the tensor singular value decomposition based on the T-product
In this paper, we present the definition of generalized tensor function according to the tensor
singular value decomposition (T-SVD) based on the tensor T-product. Also, we introduce the …
singular value decomposition (T-SVD) based on the tensor T-product. Also, we introduce the …
Multiplex transformed tensor decomposition for multidimensional image recovery
Low-rank tensor completion aims to recover the missing entries of multi-way data, which has
become popular and vital in many fields such as signal processing and computer vision. It …
become popular and vital in many fields such as signal processing and computer vision. It …
A study on T-eigenvalues of third-order tensors
W Liu, X ** - Linear Algebra and its Applications, 2021 - Elsevier
In this paper, we study T-eigenvalues of third-order tensors. Definitions of the T-eigenvalues
and Hermitian tensors are proposed. We present a commutative tensor family. We prove …
and Hermitian tensors are proposed. We present a commutative tensor family. We prove …
Low CP rank and tucker rank tensor completion for estimating missing components in image data
Tensor completion recovers missing components of multi-way data. The existing methods
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …
T-Jordan canonical form and T-Drazin inverse based on the T-product
In this paper, we investigate the tensor similarity and propose the T-Jordan canonical form
and its properties. The concepts of the T-minimal polynomial and the T-characteristic …
and its properties. The concepts of the T-minimal polynomial and the T-characteristic …
Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate
This paper investigates a demanding and meaningful task of intelligent fault diagnosis, in
which multi-sensors signals are fused for semi-supervised analysis with few labeled fault …
which multi-sensors signals are fused for semi-supervised analysis with few labeled fault …
Multi-view MERA subspace clustering
Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in
the self-representation tensor. Current tensor decompositions for MSC suffer from highly …
the self-representation tensor. Current tensor decompositions for MSC suffer from highly …
Tensor decompositions: computations, applications, and challenges
Many classical data processing techniques rely on the representation and computation of
vector and matrix forms, where the vectorization or matricization is often employed on …
vector and matrix forms, where the vectorization or matricization is often employed on …