Incremental Singular Value Decomposition Using Extended Power Method

2018 ◽  
pp. 87-105
Author(s):  
Sharad Gupta ◽  
Sudip Sanyal
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Power Method ◽  
Value Decomposition ◽  
Extended Power

Calculation of Fourth-Order Tensor Product Expansion by Power Method and Comparison of it with Higher-Order Singular Value Decomposition

2013 ◽  
Vol 444-445 ◽  
pp. 703-711
Author(s):  
Akio Ishida ◽  
Takumi Noda ◽  
Jun Murakami ◽  
Naoki Yamamoto ◽  
Chiharu Okuma
Keyword(s):  
Singular Value Decomposition ◽  
Tensor Product ◽  
Fourth Order ◽  
Computation Time ◽  
Higher Order ◽  
Singular Value ◽  
Multidimensional Data ◽  
Power Method ◽  
Order Tensor ◽  
Value Decomposition

Higher-order singular value decomposition (HOSVD) is known as an effective technique to reduce the dimension of multidimensional data. We have proposed a method to perform third-order tensor product expansion (3OTPE) by using the power method for the same purpose as HOSVD, and showed that our method had a better accuracy property than HOSVD, and furthermore, required fewer computation time than that. Since our method could not be applied to the tensors of fourth-order (or more) in spite of having those useful properties, we extend our algorithm of 3OTPE calculation to forth-order tensors in this paper. The results of newly developed method are compared to those obtained by HOSVD. We show that the results follow the same trend as the case of 3OTPE.

scholarly journals Image Compression Based on Block SVD Power Method

2019 ◽  
Vol 29 (1) ◽  
pp. 1345-1359
Author(s):  
Khalid El Asnaoui
Keyword(s):  
Singular Value Decomposition ◽  
Image Compression ◽  
Error Resilience ◽  
Singular Value ◽  
Network Storage ◽  
Power Method ◽  
Compression Algorithms ◽  
Compression Performance ◽  
Value Decomposition ◽  
And Storage

Abstract In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more significant for reducing data redundancy in order to allow more transfer and storage of data. In this context, this paper addresses the problem of lossy image compression. Indeed, this new proposed method is based on the block singular value decomposition (SVD) power method that overcomes the disadvantages of MATLAB’s SVD function in order to make a lossy image compression. The experimental results show that the proposed algorithm has better compression performance compared with the existing compression algorithms that use MATLAB’s SVD function. In addition, the proposed approach is simple in terms of implementation and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition
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