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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fast training and real-time classification algorithm based on Principal Component Analysis and F-transform
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
—
Number of pages
6
Pages from-to
275-280
Publisher name
IEEE
Place of publication
—
Event location
Toyama
Event date
Dec 5, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000470750300045

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