DimStiller: Workflows for Dimensional Analysis and Reduction294 dim 294 DIMS 38 Select Reduce:PCA...

DimStiller: Workflows for Dimensional Analysis and Reduction

Stephen Ingram, Tamara Munzner

Veronika Irvine, Melanie Tory

Steven Bergner, Torsten Möller

1

Overview

• Dimensionality Reduction

• Users

• Related Work

• Guidance

• DimStiller

2

Dimension(ality) Reduction

3

Dimension ReductionFilter Synthesize

CollectCull

complex combinations of input dimensions(nuttiness, fruitiness)

dimensions are redundant

(caffeine + jitteriness)

dimensions are uninteresting

(weight of spoon)

http://commons.wikimedia.org/wiki/File:A_small_cup_of_coffee.JPG

4

http://api.ning.com/files/Gbc35LODOU25XeTeFJTf-J4a9yCLyeENQYyDV-WhmYU7-BtAkehxTvfxMzK8RoLf3v0JhACwOAMIYFCbNyk1kYB-naNWGTW5/toomuchcoffee.gif

http://www.luxuo.com/wp-content/uploads/2009/03/coffee_tongue.jpg

Dimension ReductionFilter Synthesize

CollectCull

complex combinations of input dimensions(nuttiness, fruitiness)

dimensions are redundant

(caffeine + jitteriness)

dimensions are uninteresting

(weight of spoon)

http://commons.wikimedia.org/wiki/File:A_small_cup_of_coffee.JPG

5

http://api.ning.com/files/Gbc35LODOU25XeTeFJTf-J4a9yCLyeENQYyDV-WhmYU7-BtAkehxTvfxMzK8RoLf3v0JhACwOAMIYFCbNyk1kYB-naNWGTW5/toomuchcoffee.gif

http://www.luxuo.com/wp-content/uploads/2009/03/coffee_tongue.jpg

Dimension ReductionFilter Synthesize

CollectCull

complex combinations of input dimensions(nuttiness, fruitiness)

dimensions are redundant

(caffeine + jitteriness)

dimensions are uninteresting

(weight of spoon)

http://commons.wikimedia.org/wiki/File:A_small_cup_of_coffee.JPG

6

http://api.ning.com/files/Gbc35LODOU25XeTeFJTf-J4a9yCLyeENQYyDV-WhmYU7-BtAkehxTvfxMzK8RoLf3v0JhACwOAMIYFCbNyk1kYB-naNWGTW5/toomuchcoffee.gif

http://www.luxuo.com/wp-content/uploads/2009/03/coffee_tongue.jpg

Dimension ReductionFilter Synthesize

CollectCull

complex combinations of input dimensions(nuttiness, fruitiness)

dimensions are redundant

(caffeine + jitteriness)

dimensions are uninteresting

(weight of spoon)

http://commons.wikimedia.org/wiki/File:A_small_cup_of_coffee.JPG

7

http://api.ning.com/files/Gbc35LODOU25XeTeFJTf-J4a9yCLyeENQYyDV-WhmYU7-BtAkehxTvfxMzK8RoLf3v0JhACwOAMIYFCbNyk1kYB-naNWGTW5/toomuchcoffee.gif

http://www.luxuo.com/wp-content/uploads/2009/03/coffee_tongue.jpg

Dimension ReductionFilter Synthesize

CollectCull

complex combinations of input dimensions(nuttiness, fruitiness)

dimensions are redundant

(caffeine + jitteriness)

dimensions are uninteresting

(weight of spoon)

http://commons.wikimedia.org/wiki/File:A_small_cup_of_coffee.JPG

8

http://api.ning.com/files/Gbc35LODOU25XeTeFJTf-J4a9yCLyeENQYyDV-WhmYU7-BtAkehxTvfxMzK8RoLf3v0JhACwOAMIYFCbNyk1kYB-naNWGTW5/toomuchcoffee.gif

http://www.luxuo.com/wp-content/uploads/2009/03/coffee_tongue.jpg

Synthetic DR Example

http://isomap.stanford.edu/web3.jpg

Face Image Dataset:700 Faces

35x35=1225 Dimensions700 x 1225 Dataset

1

2

3

700

9

Synthetic DR Example

http://isomap.stanford.edu/web3.jpg10

New Dataset:700 Faces

2 Dimensions700 x 2 Dataset

USERS

11

Visual High Dimensional Analysis (VHDA) User Map

Math / Stats

Data Knowledge12

VHDA User Map

Math / Stats

Data Knowledge

What’s a mean?

Took Stats in Undergrad

Best Paper at NIPS

13

VHDA User Map

Math / Stats

Data Knowledge

Dropped in lap

Total Information Awareness

14

VHDA User Map

Math / Stats

Data Knowledge

Pedagogical

15

VHDA User Map

Math / Stats

Data Knowledge

Don’t Need Analysis

16

VHDA User Map

Math / Stats

Data Knowledge

Well Defined Tasks

17

VHDA User Map

Math / Stats

Data Knowledge

Middle Ground Users

18

RELATED WORK

19

Other Systems

20

Tool Target Users Limitations

Matlab, R, etc.Needs Power

Users

DR ToolkitsOnly Less

Programming

XMDVTool, GGobiNo Guidance Beyond Vis

Johansson & Johansson 2009 No Synthetic DR

Hole In Prev Work

• Access To Range Of DR Algos

• Guidance For Middle Ground Users

Contributions

22

Design and Implementation of

DimStiller

23

Global and Local Guidance

Attrib:Color Data:Normalize Reduce:PCA View:SPLOM

Global : Workflows

Local: Operators

24

GUIDANCE

25

Sloppy,Misunderstood

Compact,Evocative

26

Operator Space

Which Operations and What Order?

Sloppy,Misunderstood

Compact,Evocative

http://www.cs.cornell.edu/courses/cs322/2008sp/schedule.html

http://www.statmethods.net/advgraphs/images/corrgram3.png

http://en.wikibooks.org/wiki/File:Scree_plot_for_the_initial_dataset_Figure_36.jpg

http://www.scielo.cl/scielo.php?pid=S0716-078X2001000200019&script=sci_arttext

PCA

Correlation

MDS

Variance

Filter

http://www.iconfinder.com/icondetails/44818/400/data_filter_icon?r=1

http://www.personality-project.org/R/

SPLOM

27

Operator Space

Global GuidanceWhich Operations and What Order?

28

http://www.cs.cornell.edu/courses/cs322/2008sp/schedule.html

http://www.statmethods.net/advgraphs/images/corrgram3.png

http://en.wikibooks.org/wiki/File:Scree_plot_for_the_initial_dataset_Figure_36.jpg

http://www.scielo.cl/scielo.php?pid=S0716-078X2001000200019&script=sci_arttext

http://www.iconfinder.com/icondetails/44818/400/data_filter_icon?r=1

http://www.personality-project.org/R/

Sloppy,Misunderstood

PCA

Correlation

MDS

Variance

Filter

SPLOM

Operator Space

Compact,Evocative

Sloppy,Misunderstood

PCA

Correlation

MDS

Variance

Filter

SPLOM

Operator Space

Compact,Evocative

Local GuidanceWhat to do with a given operator?

PCA

How many principal components?

What do they mean?

29

http://www.cs.cornell.edu/courses/cs322/2008sp/schedule.html

http://www.statmethods.net/advgraphs/images/corrgram3.png

http://en.wikibooks.org/wiki/File:Scree_plot_for_the_initial_dataset_Figure_36.jpg

http://www.scielo.cl/scielo.php?pid=S0716-078X2001000200019&script=sci_arttext

http://www.iconfinder.com/icondetails/44818/400/data_filter_icon?r=1

http://www.personality-project.org/R/

DIMSTILLER

30

DimStiller

31

DimStiller

32

Workflow Selector

DimStiller

33

ExpressionTree

DimStiller

34

Operator Control

DimStiller

35

OperatorViews

EXAMPLE

36

37

5000 pts294 dim

294 DIMS

38

SelectReduce:PCAWorkflow

View Operator List Here

294 DIMS

39

Scree Plotof Variances

Cull:VarianceOperator

294 DIMS

40

Log-scalefor betterVisibility

294 DIMS

41

Choose firstnonzerodimension(31)

List of Culled Dims

264 DIMS

42

Data:NormOperator

264 DIMS

43

Correlation Sliderset to 1.0

146 DIMS

44

Correlation Sliderset to 0.9

37 DIMS

45

Eigenvalue Scree Plot : values die off

around 16

16 DIMS

46

Manageable SPLOM

16 DIMS

Operators & Workflows

47

Operator Families

Family Name Operators

Cull Variance, Name

Collect Pearson’s

Reduce PCA, MDS

View SPLOM, Histo

Attrib Color, Cluster

Filter Value

48

Custom Workflows

49

• Three Workflows Given

• Freeform Experimenting With Operators

• Custom Workflows after Success

Conclusions

• Presented the design and implementation of the DimStiller software

• Provided Global and Local guidance to open up dimensionality reduction for middle ground users

• beyond experts in math AND data

Thanks!

• Download DimStiller at ...

• Doing Dim Reduction? Let me know!

• Funded By NSERC

http://www.cs.ubc.ca/~sfingram/dimstiller

sfingram@cs.ubc.ca

51