Wizualizacja seminarium 1

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Paweł Sikora

seminarium dyplomowe

Education

Wizualizacja dużych zbiorów danych za
pomocą głębokich sieci neuronowych
AUTOR - PAWEŁ SIKORA
PROMOTOR - DR INŻ. MARCIN KURDZIEL

Dziedzina pracy
Dane: Macierz podobieństwa między
obiektami
Ustawiamy obiekty na płaszczyźnie tak aby
ich ułożenie odwzorowywało podobieństwa
Przykłady obiektów:
obrazek o rozdzielczości 20x20 – float[400]
 autorzy artykułów – ile mają wspólnych publikacji

Po co wizualizacja (redukcja
wymiarowości)
 Umożliwienie ludziom błyskawicznego
przeglądania ogromnych ilości danych
 Ekstrakcja cech do dalszej analizy danych
(klasyfikacji, klastrowania)

Cel pracy
 Wydajna implementacja algorytmu Parametric
TSNE na karcie graficznej
 Sprawdzenie efektywności algorytmu w różnych
przypadkach testowych
 Próba ulepszenia algorytmu

Visualizing data using t-SNE
 Van der Maaten, G Hinton - Journal of Machine
Learning, 2008, 750 cytowań
 Dużo lepsze wyniki od poprzednich metod

Learning a Parametric Embedding
by Preserving Local Structure.
 Van der Maaten. In Proceedings of the Twelfth
International Conference on Artificial
Intelligence & Statistics, 2009. 67 cytowań
 Opis algorytmu implementowanego w ramach
pracy
 Otrzymujemy funkcje mapującą
 Podział danych na mini batche
 Bardziej złożony od zwykłego T-SNE

Visualizing non-metric similarities
in multiple maps
 Van der Maaten and Hinton, 2012. Machine
learning, 19 cytowań
 Tworzenie wielu wizualizacji, które się uzupełniają
 Rozszerzenie T-SNE
 http://homepage.tudelft.nl/19j49/multiplemaps/
Multiple_maps_t-SNE/Multiple_maps_t-SNE.html

Inne parametryczne rozszerzenia
T-SNE
 Linear basis-function t-sne for fast nonlinear
dimensionality reduction - A Gisbrecht, B Mokbel
- Neural Networks (IJCNN), 2012, 8 cytowań
 Parametric nonlinear dimensionality reduction
using kernel t-SNE - A Gisbrecht, A Schulz, B
Hammer, Neurocomputing, 2015, 2 cytowania

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Wizualizacja seminarium 1

1. Wizualizacja dużych zbiorów danych za pomocą głębokich sieci neuronowych AUTOR - PAWEŁ SIKORA PROMOTOR - DR INŻ. MARCIN KURDZIEL

2. Dziedzina pracy Dane: Macierz podobieństwa między obiektami Ustawiamy obiekty na płaszczyźnie tak aby ich ułożenie odwzorowywało podobieństwa Przykłady obiektów: obrazek o rozdzielczości 20x20 – float[400]  autorzy artykułów – ile mają wspólnych publikacji

4. Po co wizualizacja (redukcja wymiarowości)  Umożliwienie ludziom błyskawicznego przeglądania ogromnych ilości danych  Ekstrakcja cech do dalszej analizy danych (klasyfikacji, klastrowania)

5. Cel pracy  Wydajna implementacja algorytmu Parametric TSNE na karcie graficznej  Sprawdzenie efektywności algorytmu w różnych przypadkach testowych  Próba ulepszenia algorytmu

6. Visualizing data using t-SNE  Van der Maaten, G Hinton - Journal of Machine Learning, 2008, 750 cytowań  Dużo lepsze wyniki od poprzednich metod

7. Learning a Parametric Embedding by Preserving Local Structure.  Van der Maaten. In Proceedings of the Twelfth International Conference on Artificial Intelligence & Statistics, 2009. 67 cytowań  Opis algorytmu implementowanego w ramach pracy  Otrzymujemy funkcje mapującą  Podział danych na mini batche  Bardziej złożony od zwykłego T-SNE

8. Visualizing non-metric similarities in multiple maps  Van der Maaten and Hinton, 2012. Machine learning, 19 cytowań  Tworzenie wielu wizualizacji, które się uzupełniają  Rozszerzenie T-SNE  http://homepage.tudelft.nl/19j49/multiplemaps/ Multiple_maps_t-SNE/Multiple_maps_t-SNE.html

9. Inne parametryczne rozszerzenia T-SNE  Linear basis-function t-sne for fast nonlinear dimensionality reduction - A Gisbrecht, B Mokbel - Neural Networks (IJCNN), 2012, 8 cytowań  Parametric nonlinear dimensionality reduction using kernel t-SNE - A Gisbrecht, A Schulz, B Hammer, Neurocomputing, 2015, 2 cytowania

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Wizualizacja seminarium 1

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Wizualizacja seminarium 1