A Comparison of Differential Evolution and Genetic Algorithms for the Column Subset Selection Problem

2016 ◽  
pp. 223-232 ◽  
Author(s):  
Pavel Krömer ◽  
Jan Platoš
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Subset Selection ◽  
Selection Problem ◽  
Column Subset Selection

Optimal column subset selection for image classification by genetic algorithms

2016 ◽  
Vol 265 (2) ◽  
pp. 205-222 ◽  
Author(s):  
Pavel Krömer ◽  
Jan Platoš ◽  
Jana Nowaková ◽  
Václav Snášel
Keyword(s):  
Genetic Algorithms ◽  
Image Classification ◽  
Subset Selection ◽  
Column Subset Selection ◽  
Selection For ◽  
Optimal Column

scholarly journals Optimality of Spectrum Pursuit for Column Subset Selection Problem: Theoretical Guarantees and Applications in Deep Learning

2020 ◽  
Author(s):  
Mohsen Joneidi ◽  
Saeed Vahidian ◽  
Ashkan Esmaeili ◽  
Siavash Khodadadeh
Keyword(s):  
Deep Learning ◽  
Upper Bound ◽  
Linear Complexity ◽  
Subset Selection ◽  
Selection Problem ◽  
Theoretical Methods ◽  
Novel Technique ◽  
Original Dataset ◽  
Column Subset Selection ◽  
Minimum Number

We propose a novel technique for finding representatives from a large, unsupervised dataset. The approach is based on the concept of self-rank, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation. Our proposed algorithm enjoys linear complexity w.r.t. the size of original dataset and simultaneously it provides an adaptive upper bound for approximation ratio. These favorable characteristics result in filling a historical gap between practical and theoretical methods in finding representatives.<br>

scholarly journals Optimality of Spectrum Pursuit for Column Subset Selection Problem: Theoretical Guarantees and Applications in Deep Learning

2020 ◽  
Author(s):  
Mohsen Joneidi ◽  
Saeed Vahidian ◽  
Ashkan Esmaeili ◽  
Siavash Khodadadeh
Keyword(s):  
Deep Learning ◽  
Upper Bound ◽  
Linear Complexity ◽  
Subset Selection ◽  
Selection Problem ◽  
Theoretical Methods ◽  
Novel Technique ◽  
Original Dataset ◽  
Column Subset Selection ◽  
Minimum Number

We propose a novel technique for finding representatives from a large, unsupervised dataset. The approach is based on the concept of self-rank, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation. Our proposed algorithm enjoys linear complexity w.r.t. the size of original dataset and simultaneously it provides an adaptive upper bound for approximation ratio. These favorable characteristics result in filling a historical gap between practical and theoretical methods in finding representatives.<br>

A Distributed Integrated Feature Selection Scheme for Column Subset Selection

2021 ◽  
pp. 1-1
Author(s):  
Zheng Xiao ◽  
Pengcheng Wei ◽  
Anthony Chronopoulos ◽  
Anne C. Elster
Keyword(s):  
Feature Selection ◽  
Subset Selection ◽  
Selection Scheme ◽  
Column Subset Selection

scholarly journals Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 517-527 ◽  
Author(s):  
Xiaoyan Luo ◽  
Zhiqi Shen ◽  
Rui Xue ◽  
Han Wan
Keyword(s):  
Subset Selection ◽  
Selection Method ◽  
Band Selection ◽  
Column Subset Selection
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