Preprocessing COVID-19 Radiographic Images by Evolutionary Column Subset Selection

2020 ◽  
pp. 425-436
Author(s):  
Jana Nowaková ◽  
Pavel Krömer ◽  
Jan Platoš ◽  
Václav Snášel
Keyword(s):  
Subset Selection ◽  
Radiographic Images ◽  
Column Subset Selection

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

scholarly journals Column subset selection is NP-complete

2021 ◽  
Vol 610 ◽  
pp. 52-58
Author(s):  
Yaroslav Shitov
Keyword(s):  
Subset Selection ◽  
Column Subset Selection ◽  
Np Complete

scholarly journals Column subset selection via sparse approximation of SVD

2012 ◽  
Vol 421 ◽  
pp. 1-14 ◽  
Author(s):  
A. Çivril ◽  
M. Magdon-Ismail
Keyword(s):  
Subset Selection ◽  
Sparse Approximation ◽  
Column Subset Selection

scholarly journals Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract)

2021 ◽  
Author(s):  
Michał Dereziński ◽  
Rajiv Khanna ◽  
Michael W. Mahoney
Keyword(s):  
Subset Selection ◽  
Low Rank ◽  
Data Matrix ◽  
Worst Case Analysis ◽  
Nyström Method ◽  
Worst Case ◽  
Nystrom Method ◽  
Column Subset Selection ◽  
Low Rank Approximations ◽  
The Cost

The Column Subset Selection Problem (CSSP) and the Nystrom method are among the leading tools for constructing interpretable low-rank approximations of large datasets by selecting a small but representative set of features or instances. A fundamental question in this area is: what is the cost of this interpretability, i.e., how well can a data subset of size k compete with the best rank k approximation? We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees which go beyond the standard worst-case analysis. Our approach leads to significantly better bounds for datasets with known rates of singular value decay, e.g., polynomial or exponential decay. Our analysis also reveals an intriguing phenomenon: the cost of interpretability as a function of k may exhibit multiple peaks and valleys, which we call a multiple-descent curve. A lower bound we establish shows that this behavior is not an artifact of our analysis, but rather it is an inherent property of the CSSP and Nystrom tasks. Finally, using the example of a radial basis function (RBF) kernel, we show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.

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

Distributed Column Subset Selection on MapReduce

2013 ◽  
Author(s):  
Ahmed K. Farahat ◽  
Ahmed Elgohary ◽  
Ali Ghodsi ◽  
Mohamed S. Kamel
Keyword(s):  
Subset Selection ◽  
Column Subset Selection

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>

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