Novel dimensionality reduction approach for unsupervised learning on small datasets

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Novel dimensionality reduction approach for unsupervised learning on small datasets
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics

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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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