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Interactive Model Learning from High-Dimensional Data: A Visual Analytics Approach Klaus Mueller Klaus Mueller Computer Science Lab for Visual Analytics and Imaging (VAI) Stony Brook University Visual Analytics Visual Analytics (Laymans

  1. Interactive Model Learning from High-Dimensional Data: A Visual Analytics Approach Klaus Mueller Klaus Mueller Computer Science Lab for Visual Analytics and Imaging (VAI) Stony Brook University

  2. Visual Analytics

  3. Visual Analytics (Layman’s View)

  4. Visual Analytics (Layman’s View)

  5. Visual Analytics (Layman’s View)

  6. Visual Analytics (Layman’s View)

  7. Visual Analytics (Layman’s View)

  8. Visual Analytics (Expert View) Visual Interface Computer Human Data

  9. Visual Analytics (Expert View) Visual Interface Computer Human computing hardware manage algorithms Data

  10. Visual Analytics (Expert View) Visual Interface Computer Human computing hardware pattern recognition manage algorithms creative thought Data

  11. Visual Analytics (Expert View) Visual Interface Computer Human computing hardware pattern recognition manage algorithms creative thought Data mental model abstracted knowledge

  12. Visual Analytics (Expert View) Visual Interface Computer Human computing hardware pattern recognition manage algorithms creative thought Data formal model mental model formatted knowledge abstracted knowledge

  13. Visual Analytics (Expert View) Visual Interface Computer Human computing hardware pattern recognition manage algorithms creative thought Data formal model mental model formatted knowledge abstracted knowledge formalized insight

  14. Visual Analytics (Expert View) update Visual Interface visualize Computer Human computing hardware pattern recognition manage algorithms creative thought Data formal model mental model formatted knowledge abstracted knowledge

  15. Visual Analytics (Expert View) interact Visual Interface learn Computer Human computing hardware pattern recognition manage algorithms creative thought apply/update Data formal model mental model formatted knowledge abstracted knowledge

  16. Visual Analytics (Expert View) update Visual Interface visualize Computer Human computing hardware pattern recognition manage algorithms creative thought apply/update Data formal model mental model formatted knowledge abstracted knowledge

  17. Visual Analytics (Expert View) update interact Visual Interface learn visualize Computer Human computing hardware pattern recognition manage algorithms creative thought apply/update apply/update Data formal model mental model formatted knowledge abstracted knowledge

  18. Visual Analytics (Expert View) update interact Visual Interface visual communication learn visualize Computer Human computing hardware pattern recognition manage algorithms creative thought apply/update apply/update Data formal model mental model formatted knowledge abstracted knowledge Mueller, et al. IEEE CG&A, 2011

  19. Visual Communication Obviously, the better a communicator the computer is, the better the learnt model • computer communicates its current model via visualizations • analyst critiques it via visual interactions • computer learns a better model • and so on…

  20. Visual Communication Obviously, the better a communicator the computer is, the better the learnt model • computer communicates its current model via visualizations • analyst critiques it via visual interactions • computer learns a better model • and so on… A key question is thus: • can computers master the art of communication?

  21. Visual Communication Obviously, the better a communicator the computer is, the better the learnt model • computer communicates its current model via visualizations • analyst critiques it via visual interactions • computer learns a better model • and so on… A key question is thus: • can computers master the art of communication? Good visual design and interaction is important Mueller, et al. IEEE CG&A, 2011

  22. Visual Model Sculpting Some motivating quotes from Michelangelo: I saw the angel in the marble and carved until I set him free. Every block of stone has a statue inside it and it is the task of the sculptor to discover it. The marble not yet carved can hold the form of every thought the greatest artist has.

  23. Visual Model Sculpting Some motivating quotes from Michelangelo: I saw the angel in the marble and carved until I set him free. Every block of stone has a statue inside it and it is the task of the sculptor to discover it. The marble not yet carved can hold the form of every thought the greatest artist has. Exchange ‘angel’ or ‘statue’ by ‘model’ and you can be the Michelangelo of Visual Analytics 

  24. Differences Michelangelo’s ‘data’ were 3-D blocks of marble • ours are N-D blocks of bytes Michelangelo’s tools were chisels, etc. • ours are mouse, multi-touch devices, etc Michelangelo would say things like this: • “It is well with me only when I have a chisel in my hand. “

  25. High-D Visualization Problems • comprehensive high-D visualizations can be very confusing • need to make high-D visualization user friendly and intuitive

  26. High-D Visualization Problems • comprehensive high-D visualizations can be very confusing • need to make high-D visualization user friendly and intuitive Key elements towards these goals • interactive: allow users to playfully sculpt the knowledge • communicative: let the data tell their story • illustrative: abstract away irrelevant detail • grounded: maintain a reference to native data space

  27. High-D Visualization Problems • comprehensive high-D visualizations can be very confusing • need to make high-D visualization user friendly and intuitive Key elements towards these goals • interactive: allow users to playfully sculpt the knowledge • communicative: let the data tell their story • illustrative: abstract away irrelevant detail • grounded: maintain a reference to native data space Four (somewhat) complementary paradigms • spectral plots  see high-D hierarchies • dynamic scatterplots  see high-D shapes • parallel coordinates  see high-D cause + effect • space embeddings  see high-D relationships

  28. Spectral Plots (SpectrumMiner) shown: 7076 particles of 450-D mass spectra acquired with single particle mass spectrometer (SPLAT)

  29. N-D Sculpting w/SpectrumMiner reducing the effect of sodium (set weight = 0.1)

  30. N-D Sculpting w/SpectrumMiner reducing the effect of sodium (set weight = 0.1) 3D PCA view Garg, Nam, Ramakrishnan, Mueller, IEEE VAST 2008

  31. N-D Sculpting w/SpectrumMiner reducing the effect of sodium (set weight = 0.1) 3D PCA view user chooses k=5 automated k-means

  32. N-D Sculpting w/SpectrumMiner reducing the effect of sodium (set weight = 0.1) 3D PCA view user chooses k=5 inspect more closely automated k-means

  33. N-D Sculpting w/SpectrumMiner show dimension interactions in neighborhood map Nam, Zelenyuk, Imre, Mueller, IEEE VAST 2007

  34. N-D Sculpting w/SpectrumMiner show dimension interactions in neighborhood map before merge after merge

  35. N-D Sculpting w/SpectrumMiner Support Vector Machine (SVM) Model encodes this knowledge show dimension interactions in neighborhood map before merge after merge

  36. Scatterplots Familiar for the display of bi-variate relationships

  37. Scatterplots Familiar for the display of bi-variate relationships Multivariate relationships arranged in scatterplot matrices • not overly intuitive to perceive multivariate relationships

  38. Dynamic Scatterplots Interaction to help ‘see’ N-D • user interface is key  N-D Navigator TM

  39. Dynamic Scatterplots Interaction to help ‘see’ N-D • user interface is key  N-D Navigator TM Motion parallax beats stereo for 3D shape perception • the same is true for N-D shape perception • help perception by illustrative motion blur

  40. Dynamic Scatterplots Interaction to help ‘see’ N-D • user interface is key  N-D Navigator TM Motion parallax beats stereo for 3D shape perception • the same is true for N-D shape perception • help perception by illustrative motion blur

  41. Dynamic Scatterplots Elemental component is the polygonal touchpad • allows navigation of projection plane in N-D space • get axis vectors using generalized barycentric interpolation   y-axis    cot( ) cot( )       w 3  2 || || p v 3   N N  where = p a v a w w i i i i k   1 1 i k x-axis Garg, Nam, Ramakrishnan, Mueller, IEEE VAST 2008

  42. Application: Cluster Analysis Step 1: • dimension reduction using subspace clustering Step 2: • visit each subspace • initialize projective view using projection pursuit • set up touchpad Step 3: • lift-off… Nam, Mueller, (submitted) IEEE TVCG, 2010

  43. Video

  44. Locating Interesting Patterns – Dynamic Display Initial view All packets have source port 80. Garg, Nam, Ramakrishnan, Mueller, VAST 2008

  45. Locating Interesting Patterns – Dynamic Display Random Coloring

  46. Locating Interesting Patterns – Dynamic Display Zooming

  47. Locating Interesting Patterns – Dynamic Display Moving the Y Axis between Src_IP and Time dimension Same Color: Same Src_IP and Dest_IP

  48. Locating Interesting Patterns – Dynamic Display To overcome the overlap, twist the X- axis a bit. Separate different packet groups.

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