Fast training and real-time classification algorithm based on Principal Component Analysis and F-transform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F18%3AA2001V0S" target="_blank" >RIV/61988987:17610/18:A2001V0S - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SCIS-ISIS.2018.00056" target="_blank" >http://dx.doi.org/10.1109/SCIS-ISIS.2018.00056</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SCIS-ISIS.2018.00056" target="_blank" >10.1109/SCIS-ISIS.2018.00056</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast training and real-time classification algorithm based on Principal Component Analysis and F-transform

Popis výsledku v původním jazyce

While machine learning algorithms become more and more accurate in image processing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, we are considering the computation complexity of a machine learning algorithm training/classification phase as the major criterion. The main aim is given to the Principal Component Analysis algorithm, which is examined, its drawbacks are point-out and suppressed by the proposed combination with the F-transform technique. We show that the training phase of such a combination is very fast, which is caused by the fact that both PCA and F-transform algorithms reduce dimensionality. In the designed benchmark, we show that the success rate of the fast hybrid algorithm is the same as the original PCA, due to F-transform ability to capture spatial information and reduction of noise/distortion in an image. Finally, we demonstrate that PCA+FT is faster and can achieve a higher success rate than a standard Convolution Neural Network and nevertheless, it is slightly less accurate as a Capsule Neural Network for the chosen dataset, its training phase is 100000x faster and classification time is faster 9x.

Název v anglickém jazyce

Fast training and real-time classification algorithm based on Principal Component Analysis and F-transform

Popis výsledku anglicky

While machine learning algorithms become more and more accurate in image processing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, we are considering the computation complexity of a machine learning algorithm training/classification phase as the major criterion. The main aim is given to the Principal Component Analysis algorithm, which is examined, its drawbacks are point-out and suppressed by the proposed combination with the F-transform technique. We show that the training phase of such a combination is very fast, which is caused by the fact that both PCA and F-transform algorithms reduce dimensionality. In the designed benchmark, we show that the success rate of the fast hybrid algorithm is the same as the original PCA, due to F-transform ability to capture spatial information and reduction of noise/distortion in an image. Finally, we demonstrate that PCA+FT is faster and can achieve a higher success rate than a standard Convolution Neural Network and nevertheless, it is slightly less accurate as a Capsule Neural Network for the chosen dataset, its training phase is 100000x faster and classification time is faster 9x.

Druh

D - Stať ve sborníku

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS)

ISBN

978-1-5386-2633-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

275-280

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Toyama

Datum konání akce

5. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000470750300045