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On_Asymptotic_Optimality_of_Self_Representative_Low_Rank_Approximation_and_Its_Applications(1).pdf (1.14 MB)

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

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posted on 20.11.2020, 03:29 by Mohsen Joneidi, Saeed Vahidian, Ashkan Esmaeili, Siavash Khodadadeh
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.

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Email Address of Submitting Author

mohsen.joneidi@ucf.edu

Submitting Author's Institution

University of Central Florida

Submitting Author's Country

United States of America

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