Intelligent Database Systems Lab

Presenter : Chuang, Kai-Ting

Authors : Rodrigo T. Peres, Claus Aranha,

Carlos E. Pedreira

2013, InfSci

Optimized bi-dimensional data projection for clustering visualization

Intelligent Database Systems Lab

Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab

Motivation• The problem of data visualization consists of

generating a bi-dimensional projection of a high-

dimensional data set.

Intelligent Database Systems Lab

Objectives• We propose a new method to project n-dimensional

data onto two dimensions, for visualization purposes.

• We apply Differential Evolution as a meta-heuristic to

optimize a divergence measure of the projected data.

• This divergence measure is based on the Cauchy–

Schwartz divergence, extended for multiple classes.

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Methodology-Framework

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Methodology

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Methodology-Cauchy-Schwartz divergence measure

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Methodology-Information Theoretic Learning (ITL)

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Methodology-Information Theoretic Learning (ITL)

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Methodology-Information Theoretic Learning (ITL)

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Methodology-Computational complexity of the Dcs

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Methodology-Differential Evolution

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Methodology-Data transformation

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Experiment setup• Synthetic data sets– Initial conditions.– Robustness of the method to very noisy dimesions.• Real world data sets– Pen Digits– Lung Cancer– Compares monocytes-related dendritic cells, plasmocytoid

dendritic cells and B-lymphocytes.– Compares monocytes and neutrophils.– Compares plasmocytoid dendritic cells and neutrophils .

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Experiment-Kernel width

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Experiment-Synthetic data sets1

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Experiment-Synthetic data sets2

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Experiment-Real world data sets

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Conclusions• Using this method, we promote the bi-dimensional

visualization of high-dimensional data sets with

optimized cluster separation.

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Comments• Advantages– The method performed well .• Disadvantages– It may be slower to train on data sets with a larger

number of cases. • Applications– Visualization.