Novel dimensionality reduction approach for unsupervised learning on small datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA2101WLX" target="_blank" >RIV/61988987:17610/20:A2101WLX - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0031320320300959" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0031320320300959</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patcog.2020.107291" target="_blank" >10.1016/j.patcog.2020.107291</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Novel dimensionality reduction approach for unsupervised learning on small datasets

Popis výsledku v původním jazyce

We focus on an image classification task in which only several unlabeled images per class are available for learning and low computational complexity is required. We recall the state-of-the-art methods that are used to solve the task: autoencoder-based approaches and manifold-decomposition-based approaches. Next, we introduce our proposed method, which is based on a combination of the F-transform and (kernel) principal component analysis. F-transform significantly reduces the computation time of PCA and increases the robustness of PCA to translation, while PCA proposes more descriptive features. This combination performs 3D reduction: the F-transform reduces dimensionality over a single 2D image, while PCA reduces dimensionality through the whole set of processed images. Based on the benchmark results, our method may outperform deep-learning-based methods in limited settings. For completeness, we also address other image resampling algorithms that can be used instead of the F-transform, and we find that the F-transform is the most suitable.

Název v anglickém jazyce

Novel dimensionality reduction approach for unsupervised learning on small datasets

Popis výsledku anglicky

We focus on an image classification task in which only several unlabeled images per class are available for learning and low computational complexity is required. We recall the state-of-the-art methods that are used to solve the task: autoencoder-based approaches and manifold-decomposition-based approaches. Next, we introduce our proposed method, which is based on a combination of the F-transform and (kernel) principal component analysis. F-transform significantly reduces the computation time of PCA and increases the robustness of PCA to translation, while PCA proposes more descriptive features. This combination performs 3D reduction: the F-transform reduces dimensionality over a single 2D image, while PCA reduces dimensionality through the whole set of processed images. Based on the benchmark results, our method may outperform deep-learning-based methods in limited settings. For completeness, we also address other image resampling algorithms that can be used instead of the F-transform, and we find that the F-transform is the most suitable.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PATTERN RECOGN

ISSN

0031-3203

e-ISSN

—

Svazek periodika

103

Číslo periodika v rámci svazku

červenec

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

107291

Kód UT WoS článku

000530845000026

EID výsledku v databázi Scopus

2-s2.0-85080061950