Download Experiment

Trustworthy dimension reduction for
visualization different data sets
Presenter: NENG-KAI, HONG
Authors: SAFA A. NAJIM, IK SOO LIM
2014, IS
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
1
Intelligent Database Systems Lab
Motivation
• The current dimension reduction methods used
in visualization may face the problem of false
colors.
• Metrics used to measure the trustworthiness of
each pixel in the visualization are just give only
measurement value.
2
Intelligent Database Systems Lab
Objectives
• To introduce a novel new version of SPE which
shows trustworthy behavior—TSPE.
• To add a new point-wise metric to measure the
trustworthiness of each pixel in the
visualization .
3
Intelligent Database Systems Lab
Methodology
• dimensionality reduction
4
Intelligent Database Systems Lab
Methodology
• Measurement of the quality of visualization
 Residual variance (Stress)
 Correlation function (γ)
 Local continuity (LC)
5
Intelligent Database Systems Lab
Methodology
• SPE
• TSPE
6
Intelligent Database Systems Lab
Experiment
7
Intelligent Database Systems Lab
Experiment
8
Intelligent Database Systems Lab
Experiment
9
Intelligent Database Systems Lab
Methodology
• Point-wise quality metric
10
Intelligent Database Systems Lab
Experiment
11
Intelligent Database Systems Lab
Experiment
12
Intelligent Database Systems Lab
Experiment
13
Intelligent Database Systems Lab
Experiment
14
Intelligent Database Systems Lab
Experiment
15
Intelligent Database Systems Lab
Experiment
16
Intelligent Database Systems Lab
Experiment
17
Intelligent Database Systems Lab
Experiment
18
Intelligent Database Systems Lab
Experiment
19
Intelligent Database Systems Lab
Experiment
20
Intelligent Database Systems Lab
Experiment
21
Intelligent Database Systems Lab
Experiment
22
Intelligent Database Systems Lab
Experiment
23
Intelligent Database Systems Lab
Experiment
24
Intelligent Database Systems Lab
Experiment
25
Intelligent Database Systems Lab
Experiment
26
Intelligent Database Systems Lab
Conclusions
• TSPE has a good ability to unfold the curved
cylinder data set and preserve the
neighborhood distances efficiently.
• Visualizations by TSPE can overcome the false
colors problem as well as possible.
• TSPE can discover more things which are
invisible by other methods
27
Intelligent Database Systems Lab
Comments
• Advantage
– A new way to improve dimension reduction
performance, but it still needs to be aware of
whether there’re some unsuitable data to this
method.
• Applications
– Dimensionality reduction, visualization and tensor
image
28
Intelligent Database Systems Lab