Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches-Reference-Cited by-同舟云学术

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Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches

Published:2022 Issue: Volume:70 Page:1348-1360
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Schellekens Vincent¹^ORCID,Jacques Laurent¹^ORCID

Affiliation:

1. ICTEAM/INMA, UCLouvain, Ottignies-Louvain-la-Neuve, Belgium

Funder

Belgian National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/9675017/09730051.pdf?arnumber=9730051

Reference38 articles.

1. Scikit-learn: Machine learning in python;pedregosa;J Mach Learn Res,2011

2. K-means: The advantages of careful seeding;arthur;Proc ACM-SIAM Symp Discrete Algorithms,0

3. Least squares quantization in PCM

4. PYCLE: A Python Compressive Learning Toolbox;schellekens,2020

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