Post on 27-Jun-2020
A-tSNEApproximated and user steerable for progressive visual analytics
Nicola Pezzotti et al.Presented by: Lovedeep Gondara
March 9, 2017
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 1 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 2 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 3 / 25
High dimensional data
Most real world datasets are high dimensional.
High dimensional data vis is hard.
Dimensionality reduction to the rescue.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 4 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 5 / 25
tSNEIntroduction
A tool for dimensionality reduction/vis of high dimensional data.
Converts similarities between data points in high dimensional space tojoint probability distribution P.
Computes a joint probability distribution Q, describing similarity inlow dimensional space.
Goal: Represent P faithfully using Q.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 6 / 25
tSNEIntroduction
Minimize Kullback-Leibler divergence between P and Q.
Use gradient descent for minimization.
Each point attracts or repels all other points with a force F .
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 7 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 8 / 25
Barnes-Hut SNE
Original tSNE uses brute force approach for F .
Computation and memory complexity of O(n2).
Barnes-Hut SNE is an evolution of tSNE.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 9 / 25
Barnes-Hut SNE
Uses two approximations.
Approximation 1: Similarities between data points are computed byonly taking set of nearest neighbours N.
Approximation 2: Uses Barnes-Hut algorithm.
Reduces computational and memory complexity to O(N log(N))) andO(N) respectively.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 10 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 11 / 25
A-tSNEIntroduction
Evolution of BH-SNE.
Uses approximations to generate useful intermediate results.
Approximation defined by user.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 12 / 25
A-tSNEIntroduction
Figure: Progressive visual analytics using tSNE
Figure: Progressive visual analytics using A-tSNE
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 13 / 25
A-tSNEIntroduction
Improves BH-SNE using approximated KNN computations forapproximated P.
Using a precision parameter ρ, describe the average percentage ofpoints in approximated neighbourhood that belong to the exactneighbourhood.
ρ is user defined, large values of ρ means better approximations butmore computational overhead.
These approximations make A-tSNE computationally steerable.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 14 / 25
A-tSNEIntroduction
Figure: BH-SNE: 3191.8 s
Figure: A-tSNE (ρ = 0.34): 30.1 s
Figure: A-tSNE (ρ = 0.23): 20.4 s
Figure: A-tSNE (ρ = 0.07): 13.0 s
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 15 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 16 / 25
A-tSNEUser driven refinement
User selection: Select a subset of points for immediate refinement.
Breadth first search: If only a few points are selected, include theneighbourhoods.
Density based refinement: Global overview, user defined selection orwhole dataset.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 17 / 25
A-tSNEVisualization and interaction
Density based: Simple points increase clutter, use KDE.
Visualizing approximations: Precision of high dimensional similaritiesis gradually refined until exact, requested precision can be visualizedwhile refinement is ongoing.
Use magic lens to show approximations
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 18 / 25
A-tSNEData manipulation
Inserting points
Deleting points
Dimensionality modification
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 19 / 25
A-tSNEInterface
Figure: Interface
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 20 / 25
Outline
1 InroductionHigh dimensional data vistSNEBarnes-Hut SNE
2 A-tSNEIntroductionInteractive analysisCase studies
3 Critique
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 21 / 25
A-tSNEMouse brain gene expression
Figure: Analysis of the gene expression in the mouse brain using A-tSNE
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 22 / 25
A-tSNEReal-time analysis of high-dimensional streams
Figure: Initial embedding
Figure: New cluster indicates thecreation of a set of different readings
Figure: Evolution of (a)
Figure: The cluster that identifiesmiscalibrated readings is removed
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 23 / 25
Critique
Enhanced performance.
User selective refinement.
Too many moving parts.
Not sure if all are helpful.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 24 / 25
For Further Reading I
Pezotti et al. 2016Approximated and user steerable tsne for progressive visual analytics.IEEE Transactions on Visualization and Computer Graphics, 2016.
Nicola Pezzotti et al. Presented by: Lovedeep Gondara A-tSNE March 9, 2017 25 / 25