Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

2010 ◽  
Vol 52 (9) ◽  
pp. 693-709 ◽  
Author(s):  
Sandipan Chakroborty ◽  
Goutam Saha
Keyword(s):  
Feature Selection ◽  
Singular Value Decomposition ◽  
Speaker Identification ◽  
Singular Value ◽  
Qr Factorization ◽  
Column Pivoting ◽  
Value Decomposition

scholarly journals Cache Servers Placement Based on Important Switches for SDN-Based ICN

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 39 ◽  
Author(s):  
Jan Badshah ◽  
Majed Mohaia Alhaisoni ◽  
Nadir Shah ◽  
Muhammad Kamran
Keyword(s):  
Energy Consumption ◽  
Singular Value Decomposition ◽  
Linear Algebra ◽  
Optimization Problem ◽  
Computation Time ◽  
Singular Value ◽  
Qr Factorization ◽  
Traffic Matrix ◽  
Column Pivoting ◽  
Value Decomposition

In centralized cache management for SDN-based ICN, it is an optimization problem to compute the location of cache servers and takes a longer time. We solve this problem by proposing to use singular-value-decomposition (SVD) and QR-factorization with column pivoting methods of linear algebra as follows. The traffic matrix of the network is lower-rank. Therefore, we compute the most important switches in the network by using SVD and QR-factorization with column pivoting methods. By using real network traces, the results show that our proposed approach reduces the computation time significantly, and also decreases the traffic overhead and energy consumption as compared to the existing approach.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2016 ◽  
pp. 281-304
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
New Model ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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