Publication: Studying generalization performance of random feature model through equivalent models
Program
KU-Authors
KU Authors
Co-Authors
Publication Date
Language
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
Denk modeller kullanarak rasgele öznitelik modelinin genelleme performansının incelenmesi
Abstract
Random Feature Model (RFM) has been received significant attention due to its similarity to neural networks and its relatively simple analysis. The training and generalization performances of the RFM has been shown to be equivalent to the noisy linear model under isotropic data assumption in the asymptotic limit. It is possible to analyze the performance of the RFM using the equivalent model. In this work, we focus on studying the generalization performance of the RFM using equivalent models and extending it to anisotropic data. First, we illustrate the regimes where the equivalence with the aforementioned linear model holds and where it does not hold on Fashion-MNIST dataset. Then, we observe a new equivalence for a regime where the aforementioned equivalence does not hold.
Source
Publisher
IEEE
Subject
Computer science, Electrical and electronic, Telecommunications
Citation
Has Part
Source
32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024
Book Series Title
Edition
DOI
10.1109/SIU61531.2024.10600866