Approximately orthogonal nonnegative Tucker decomposition for flexible multiway clustering

2021 ◽  
Author(s):  
YiChun Qiu ◽  
WeiJun Sun ◽  
Yu Zhang ◽  
XiaoBo Gu ◽  
GuoXu Zhou
Keyword(s):  
Tucker Decomposition ◽  
Multiway Clustering

Semi-supervised non-negative Tucker decomposition for tensor data representation

2021 ◽  
Author(s):  
YuNing Qiu ◽  
GuoXu Zhou ◽  
XinQi Chen ◽  
DongPing Zhang ◽  
XinHai Zhao ◽  
...  
Keyword(s):  
Data Representation ◽  
Tucker Decomposition ◽  
Tensor Data

scholarly journals Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yuki Takashima ◽  
Toru Nakashika ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Dictionary Learning ◽  
Computational Cost ◽  
Tensor Decomposition ◽  
Gaussian Mixture ◽  
Voice Conversion ◽  
Specific Information ◽  
Learning Method ◽  
Tucker Decomposition ◽  
Parallel Data ◽  
High Computational Cost

Abstract Voice conversion (VC) is a technique of exclusively converting speaker-specific information in the source speech while preserving the associated phonemic information. Non-negative matrix factorization (NMF)-based VC has been widely researched because of the natural-sounding voice it achieves when compared with conventional Gaussian mixture model-based VC. In conventional NMF-VC, models are trained using parallel data which results in the speech data requiring elaborate pre-processing to generate parallel data. NMF-VC also tends to be an extensive model as this method has several parallel exemplars for the dictionary matrix, leading to a high computational cost. In this study, an innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed. The proposed method uses tensor decomposition and decomposes an input observation into a set of mode matrices and one core tensor. The proposed NTD-based dictionary-learning method estimates the dictionary matrix for NMF-VC without using parallel data. The experimental results show that the proposed method outperforms other methods in both parallel and non-parallel settings.

scholarly journals On Optimizing Distributed Tucker Decomposition for Sparse Tensors

2018 ◽  
Author(s):  
Venkatesan T. Chakaravarthy ◽  
Jee W. Choi ◽  
Douglas J. Joseph ◽  
Prakash Murali ◽  
Shivmaran S. Pandian ◽  
...  
Keyword(s):  
Tucker Decomposition

scholarly journals Detection of Fatigue Microcrack Using Eddy Current Pulsed Thermography

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiang Zhang ◽  
Jianping Peng ◽  
Luquan Du ◽  
Jie Bai ◽  
Lingfan Feng ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
Eddy Current ◽  
Material Properties ◽  
Principal Component ◽  
Tucker Decomposition ◽  
Pulsed Thermography ◽  
Signal Processing Algorithms ◽  
Microcrack Detection ◽  
Processing Algorithms

Microcracks are a common metallic defect, resulting in degradation of material properties. In this paper, specimens with different fatigue microcracks were detected by eddy current pulsed thermography (ECPT). Signal processing algorithms were investigated to improve the detectability and sensitivity; principal component analysis (PCA) and Tucker decomposition were used to compare the performance of microcrack detection. It was found that both algorithms were highly adaptable. A thermal quotient was used to assess the temperature variation trend. Furthermore, the potential correspondence between crack closure and temperature change was investigated.

scholarly journals Orthogonal Nonnegative Tucker Decomposition

2021 ◽  
Vol 43 (1) ◽  
pp. B55-B81
Author(s):  
Junjun Pan ◽  
Michael K. Ng ◽  
Ye Liu ◽  
Xiongjun Zhang ◽  
Hong Yan
Keyword(s):  
Tucker Decomposition
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