Subset based error recovery

Date
2021-10-12
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Signal Processing
Print ISSN
0165-1684
Electronic ISSN
1872-7557
Publisher
Elsevier BV
Volume
191
Issue
Pages
108361- 1 - 108361- 8
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Series
Abstract

We propose a data denoising method using Extreme Learning Machine (ELM) structure which allows us to use Johnson-Lindenstrauß Lemma (JL) for preserving Restricted Isometry Property (RIP) in order to give theoretical guarantees for recovery. Furthermore, we show that the method is equivalent to a robust two-layer ELM that implicitly benefits from the proposed denoising algorithm. Current robust ELM methods in the literature involve well-studied L1, L2 regularization techniques as well as the usage of the robust loss functions such as Huber Loss. We extend the recent analysis on the Robust Regression literature to be effectively used in more general, non-linear settings and to be compatible with any ML algorithm such as Neural Networks (NN). These methods are useful under the scenario where the observations suffer from the effect of heavy noise. We extend the usage of ELM as a general data denoising method independent of the ML algorithm. Tests for denoising and regularized ELM methods are conducted on both synthetic and real data. Our method performs better than its competitors for most of the scenarios, and successfully eliminates most of the noise.

Course
Other identifiers
Book Title
Keywords
Robust networks, Extreme learning machine, Sparse recovery, Regularization, Hard thresholding
Citation
Published Version (Please cite this version)