Information Visualization and Machine Learning - Fabrice …apiacoa.org/.../teaching/visualization/introduction.pdf · · 2018-04-15Goals of this lecture 1 To give an introduction

Information Visualization and Machine Learning

Fabrice Rossi

Projet AxIS, INRIA Rocquencourt

27-28/02/2007

F. Rossi (INRIA) Infovis & ML 27-28/02/2007 1 / 172

Goals of this lecture

1 To give an introduction to Information Visualization (Infovis)enhancement methods for classical displaysspecialized displayswhy you should leverage infovis in your everyday work

2 To outline links between Infovis and Machine Learningwhy do they exist?current solutionsopen research problems

3 To give examples of successful joint researches:Machine learning methods designed for visualizationVisualization of machine learning algorithm results


Organization of this lecture

Visualization orientedEach part consists in

an introduction to some visualization techniquesan analysis of their links with machine learning

Five parts:1 Introduction to Infovis2 Scatter plots3 Geometrically-transformed displays4 Iconic and pixel based displays5 Visualization methods designed in the machine learning community


Outline of part I

Introduction

1 Information VisualizationDefinitionExamples from “everyday life with KDE”

2 Infovis goals and limitationsWhat is it used for?Limitations of Infovis and VDM

3 Links with machine learningFormal model of InfovisMachine learning


Outline of part II

Scatter plots

4 Introduction

5 Feature number reductionPrinciplesNeighborhood structure preservation

6 Overlapping reductionRendering and interactionClustering


Outline of part III

Geometrically-transformed displays

7 Introduction

8 Scatter plot matrixInteraction

9 Parallel CoordinatesOverlappingVariable order


Outline of part IV

Iconic and pixel based displays

10 Introduction

11 Iconic displaysChernoff’s facesStar glyphGlyph Positioning

12 Pixel based displaysDense pixel displaysDissimilarity matrix


Outline of part V

Machine Learning

13 Self Organizing MapPrinciplesVisualization

14 Latent Variable ModelsGeneral principlesGenerative Topographic Mapping


Part I

Introduction


Outline





Information Visualization

The use of computer-supported interactive, visualrepresentation of abstract data to amplify cognition

Card, Mackinlay & Shneiderman

Human preattentive processing capabilities:non conscious processing (no thinking involved)low level visual systemextremely fast: 200 msscalable (no browsing ⇒ sublinear scaling)feature type must match data type (e.g., hue is suitable forcategories, not real value)





Human preattentive processing capabilities:non conscious processing (no thinking involved)low level visual systemextremely fast: 200 msscalable (no browsing ⇒ sublinear scaling)feature type must match data type (e.g., hue is suitable forcategories, not real value)


Preattentive processing

Can you see a color outlier?

From “Perception in Visualization” by C. G. Healey http://www.csc.ncsu.edu/faculty/healey/PP/index.html


http://www.csc.ncsu.edu/faculty/healey/PP/index.html


Can you see a color outlier?





Can you see a shape outlier?





Can you see a shape outlier?







Tool metaphor (hammer, microscope, etc.):extending user possibilities:

more scalable processing (speed and/or volume)details enhancementmulti-source fusionetc.

under user control





Tool metaphor (hammer, microscope, etc.):extending user possibilities:

more scalable processing (speed and/or volume)details enhancementmulti-source fusionetc.

under user control


User control

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Petal.Width

Anderson's/Fisher's Iris

Scatter plot matrix ⇒ user control





Overview first, zoom and filter, and then details-on-demand

Information Seeking Mantra, Shneiderman

Interactivityenables user control:

exploration (panning)zooming3D world

reduces clutter on the screen





Overview first, zoom and filter, and then details-on-demand

Information Seeking Mantra, Shneiderman

Interactivityenables user control:

exploration (panning)zooming3D world

reduces clutter on the screen


Interactivity

Excentric Labeling:labels on demand

C. Plaisant and J.-D. Fekete, Human-Computer Interaction Lab and INRIA





Abstract data:digital data with no real world “visual” counterpart, e.g.:

soundhigh dimensional vectors

no “natural” visual representation of the data, e.g.:requests received by a web serverfile systemssource code

Infovis 6= scientific visualization (Scivis)





Abstract data:digital data with no real world “visual” counterpart, e.g.:

soundhigh dimensional vectors

no “natural” visual representation of the data, e.g.:requests received by a web serverfile systemssource code

Infovis 6= scientific visualization (Scivis)


Scientific data

Yearly ArcticTemperatureAnomaly, 2002

NASA/Goddard Space Flight Center Scientific Visualization Studio


Scientific data

Growth of a brain tumorEPIDAURE team, Inria


Scientific data

Weather forecastMétéo France


Scientific data

Satellite images(mixed data!)Google Earth


Abstract data

http://apiacoa.org/

XHTML structure of aweb page (nodes=tags)

Drawn with http://www.aharef.info/static/htmlgraph/


http://apiacoa.org/

http://www.aharef.info/static/htmlgraph/

Abstract data

Treemap of the linuxkernel (depth, size,type of files/dirs)

J.-D. Fekete, Human-Computer Interaction Lab and INRIA


Keim’s Taxonomy of Infovis methods

Three “orthogonal” axes:1 Visualization technique:

standard 2D/3D (part II)geometrically-transformed (part III)iconic (part IV)pixel based (part IV)

2 Interaction technique:zooming and panningbrushing and linkingdistortion

3 Data type:vectorstextstreesgraphs


Some examples from everyday life

Desktop programs are infovis tools for some abstract data:emails“text” filesfilesystemsimagesmusicthe webIDEetc.


Email (Kmail)


Pdf file (Kpdf)


Filesystem browsing (Konqueror)


Filesystem browsing (KDirStat)


Image database (Digikam)


Music (Amarok)


Wikipedia (Lupin’s popup)


Wikipedia (Lupin’s popup)


IDE (Eclipse)


Outline





What is it used for?

Some specific goals:easier access (learning curve):

GUI in generalFile system browsing

productivity (doing the same things but faster):IDE (on the fly documentation, multi-view, graphical programming,etc.)Monitoring (Lupin’s popup for wikipedia, treemap)On the fly search (kpdf, konqueror, etc.)

organization:Tree paradigm (sorting)Metadata (image, music, etc.)Overview


Visual data mining (VDM)

A.k.a. Visual Analytics and Visual Data Analysis:Interactive visual exploration of massive data sets:

cluster analysisoutlier detectiondependency assessmentpattern detection (repetition, sub-structure, etc.)etc.

Interactive visualization of the results of data mining algorithms:parameter tuningquality assessmentmining on the results (e.g., meta-clustering)etc.


Visual mining of the Iris dataset

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Pet

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Selected view



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Limitations of Infovis and VDM

Visual illusionsDistortion, occlusion, etc.Scalability:

number of objectsnumber of descriptorshuman scalabilitycomputer scalability

Non standard data (e.g., graphs, time series, etc.)


Grey levels

http://web.mit.edu/persci/people/adelson/index.html



Grey levels




Grid illusion

http://en.wikipedia.org/wiki/Image:Grid_illusion.svg


http://en.wikipedia.org/wiki/Image:Grid_illusion.svg

Ebbinghaus illusion

http://en.wikipedia.org/wiki/Image:Spheres.JPG


http://en.wikipedia.org/wiki/Image:Spheres.JPG

Separable?

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Separable?

xy

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The scalability issue

Vision is limited to 2 or 3 dimensionsPosition can be combined with other features:

color (intensity and hue)shape (e.g., star icon)textureetc.

But fast pre-attentive processing is limited to roughly 5 combinedfeaturesCorrelating distant things is difficultComputer screens have a “low” resolution (HD is 2 millions pixels)Complex HD interactive display requires dedicated graphic boardand associated software (OpenGL and Direct3D, Shaderlanguages)


An example (13 + 1 variables)

Can you see something?


An example (13 + 1 variables)

Can you see something?


Outline





How to scale?

Complementary solutions:interactivity (zooming, distorting, details on demand, etc.)data transformation:

interaction between objects rather than objects themselvessimilarity between objects

data simplification:reduction of the number of objects (summary, clustering, etc.)reduction of the number of characteristics (selection, projection,etc.)compact layout: one glyph per object or one pixel per measurement

data ordering:positioning related things closely on the screenone to three dimensional ordering

Obviously linked to Machine Learning (clustering, projection, etc.).


Chi & Riedl’s Operator Model

Production of the View from the Data:

Data Analytical Abstraction Visualization Abstraction View

Data Transformation Analytical Transformation

TransformationVisual Mapping

Arrows represent transformation operators.


Chi & Riedl’s Operator Model

Production of the View from the Data:

Data Analytical Abstraction Visualization Abstraction View

Data Transformation Analytical Transformation

TransformationVisual Mapping

Graph of the content

Graph of the pages

Depth first traversal tree Disk Tree

Hyperbolic Tree

Web site

Arrows represent transformation operators.


Where is machine learning?

(almost) everywhere! Two types of operators:1 data independent2 data dependent ⇒ machine learning, optimization, artificial

intelligence (AI)ML and AI operators:

Data: preprocessing, cleaning, etc.Data transformation: feature extraction, dissimilarity, etc.Visualization transformation: projection, clustering, ordering, etc.


Organization of the lecture

Description of major visualization methods for vector/table dataFor each class of methods:

LimitationsRendering & InteractivityAI problems

Major AI challenges:ClusteringFeature extractionOrdering

Many things are left out, e.g.:Non vector dataDistortion techniquesetc.


Documents

Information Visualization and Machine Learning - Fabrice …apiacoa.org/.../teaching/visualization/introduction.pdf · · 2018-04-15Goals of this lecture 1 To give an introduction