Visualization of Learning Process in Feature Space

In machine learning, the structure of feature space is an important factor that determines the performance of a model. Therefore, we can deepen our understanding of learning algorithms if we can visualize changes in the structure of feature space during the learning process. However, visualizing suc...

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Main Authors: Tomohiro Inoue, Noboru Murata, Taiki Sugiura
Format: Article
Language:English
Published: LibraryPress@UF 2023-05-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
Online Access:https://journals.flvc.org/FLAIRS/article/view/133329
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author Tomohiro Inoue
Noboru Murata
Taiki Sugiura
author_facet Tomohiro Inoue
Noboru Murata
Taiki Sugiura
author_sort Tomohiro Inoue
collection DOAJ
description In machine learning, the structure of feature space is an important factor that determines the performance of a model. Therefore, we can deepen our understanding of learning algorithms if we can visualize changes in the structure of feature space during the learning process. However, visualizing such changes is difficult because it requires dimensionality reduction while maintaining consistency with the data structure in high-dimensional space and in the temporal direction. In this study, we visualized feature changes during the learning process by capturing them as changes in the positional relationship between target features and time-invariant reference coordinates with a log-bilinear model.
format Article
id doaj-art-9dbf91de4fef4c9bacccbccba2ebddd0
institution OA Journals
issn 2334-0754
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language English
publishDate 2023-05-01
publisher LibraryPress@UF
record_format Article
series Proceedings of the International Florida Artificial Intelligence Research Society Conference
spelling doaj-art-9dbf91de4fef4c9bacccbccba2ebddd02025-08-20T01:52:22ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13332969635Visualization of Learning Process in Feature SpaceTomohiro Inoue0https://orcid.org/0009-0002-1795-0531Noboru Murata1https://orcid.org/0000-0002-4258-6877Taiki Sugiura2Waseda UniversityWaseda UniversityWaseda UniversityIn machine learning, the structure of feature space is an important factor that determines the performance of a model. Therefore, we can deepen our understanding of learning algorithms if we can visualize changes in the structure of feature space during the learning process. However, visualizing such changes is difficult because it requires dimensionality reduction while maintaining consistency with the data structure in high-dimensional space and in the temporal direction. In this study, we visualized feature changes during the learning process by capturing them as changes in the positional relationship between target features and time-invariant reference coordinates with a log-bilinear model.https://journals.flvc.org/FLAIRS/article/view/133329visualizationstochastic embeddingmappingdimensionality reduction
spellingShingle Tomohiro Inoue
Noboru Murata
Taiki Sugiura
Visualization of Learning Process in Feature Space
Proceedings of the International Florida Artificial Intelligence Research Society Conference
visualization
stochastic embedding
mapping
dimensionality reduction
title Visualization of Learning Process in Feature Space
title_full Visualization of Learning Process in Feature Space
title_fullStr Visualization of Learning Process in Feature Space
title_full_unstemmed Visualization of Learning Process in Feature Space
title_short Visualization of Learning Process in Feature Space
title_sort visualization of learning process in feature space
topic visualization
stochastic embedding
mapping
dimensionality reduction
url https://journals.flvc.org/FLAIRS/article/view/133329
work_keys_str_mv AT tomohiroinoue visualizationoflearningprocessinfeaturespace
AT noborumurata visualizationoflearningprocessinfeaturespace
AT taikisugiura visualizationoflearningprocessinfeaturespace