scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

scholarly journals Satellite image classification using proposed singular value decomposition method

2019 ◽  
Vol 13 (28) ◽  
pp. 52-67
Author(s):  
Noor Zubair Kouder
Keyword(s):  
Singular Value Decomposition ◽  
Classification Accuracy ◽  
Satellite Image ◽  
Field Work ◽  
Mean Value ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Erdas Imagine ◽  
Value Decomposition ◽  
Svd Method

In this work, satellite images for Razaza Lake and the surrounding areadistrict in Karbala province are classified for years 1990,1999 and2014 using two software programming (MATLAB 7.12 and ERDASimagine 2014). Proposed unsupervised and supervised method ofclassification using MATLAB software have been used; these aremean value and Singular Value Decomposition respectively. Whileunsupervised (K-Means) and supervised (Maximum likelihoodClassifier) method are utilized using ERDAS imagine, in order to getmost accurate results and then compare these results of each methodand calculate the changes that taken place in years 1999 and 2014;comparing with 1990. The results from classification indicated thatwater and hills are decreased, while vegetation, wet land and barrenland are increased for years 1999 and 2014; comparable with 1990.The classification accuracy was done by number of random pointschosen on the study area in the field work and geographical data thencompared with the classification results, the classification accuracy forthe proposed SVD method are 92.5%, 84.5% and 90% for years1990,1999,2014, respectivety, while the classification accuracies forunsupervised classification method based mean value are 92%, 87%and 91% for years 1990,1999,2014 respectivety.

scholarly journals Robust Singular Value Decomposition Method on Minor Outlier Data

Jurnal Varian ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 19-24
Author(s):  
Bernadhita Herindri Samodera Utami ◽  
Trisnawati Trisnawati ◽  
Rani Pratiwi ◽  
Miswan Gumanti
Keyword(s):  
Singular Value Decomposition ◽  
Predictive Value ◽  
Decomposition Method ◽  
Iteration Process ◽  
Singular Value ◽  
Data Matrix ◽  
Singular Value Decomposition Method ◽  
Outlier Data ◽  
Value Decomposition ◽  
Svd Method

In multivariate statistics, Singular Value Decomposition (SVD) for a data matrix containing outliers does not provide data that can be analyzed optimally. This study aims to overcome outlier data using the Robust Singular Value Decomposition (RSVD) method and compare it with the SVD method. The analysis using the RSVD method includes several steps, namely determining the initial predictive value of the vector u and regressing it then normalizing the estimator vector β and carrying out the iteration process until convergent results are obtained. The results of this study indicate that the RSVD for dealing with minor outliers data is not influenced by initial estimates. The RSVD method is strongly influenced by the large amount of outliers data, the more extreme outliers data, the more iterations are.

scholarly journals A modified truncation singular value decomposition method for solving ill-posed problems

2018 ◽  
Vol 13 ◽  
pp. 174830181881360 ◽  
Author(s):  
Zhenyu Zhao ◽  
Riguang Lin ◽  
Zehong Meng ◽  
Guoqiang He ◽  
Lei You ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Singular Value ◽  
Numerical Results ◽  
New Method ◽  
Truncated Singular Value Decomposition ◽  
Singular Value Decomposition Method ◽  
Ill Posed ◽  
Value Decomposition

A modified truncated singular value decomposition method for solving ill-posed problems is presented in this paper, in which the solution has a slightly different form. Both theoretical and numerical results show that the limitations of the classical TSVD method have been overcome by the new method and very few additive computations are needed.

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