High Dimensional Data Visualization

High Dimensional Data Visualization

Presented by Fabian Keller

Seminar: Large Scale Visualization

Advisor: Steffen Koch

University of Stuttgart, Summer Term 2015

MotivationWhat do you see?

16.07.2015 Fabian Keller 2

MotivationI can see…


Agenda

• Introduction

• Dimension Reduction Techniques

PCA / LLE / ISOMAP / t-SNE

• Visualization Techniques

Scatterplots / Parallel Coordinate Plots / Glyphs

• Conclusion


GoalOf dimensionality reduction

• High Dimensional Data (>>1000 dimensions)

• Reduce Dimensions (for Clustering / Learning / …)

• Extract Meaning

• Visualize and Interact

16.07.2015 Fabian Keller 5[c.f. Card et al 1999; dos Santos and Brodlie 2004]

Intrinsic DimensionalityHow many dimensions can we reduce?

2D 1D 3D 1D


Intrinsic Dimensionality: 1

Agenda

• Introduction





• Conclusion


Dimension ReductionWhat techniques are there?

DR Techniques

Linear

Principal Component

Analysis

Non-Linear

Local

Local Linear Embedding

Global

ISOMAP t-SNE


Principal Component Analysis (PCA)Eigen-*


• Linear, Global

• Find “Principal

Components”

• Minimize

Reconstruction Error

[isomorphismes, 2014]

Principal Component Analysis (PCA)Eigen-Faces


Local-Linear Embedding (LLE)Assumes the data is locally linear

• Non-Linear, Local

• Select neighbors and

approximate linearly

• Map to lower

dimension


[Roweis, 2000]

ISOMAPIsometric feature mapping


• Non-linear, Global

• K-Nearest Neighbors

• Construct

neighborhood graph

• Compute shortest

paths[Balasubramanian, 2002]

t-SNEStochastic Neighbor Embedding

• Non-linear, Global

• Uses Gaussian

similarities

• Preserves the

similarities in lower

dimensions


Agenda

• Introduction





• Conclusion


2D Scatter PlotsCommonly used

• Easy Perception

• (No) Interaction

• Limited to two

dimensions

• Colors?!


2D Scatter Plot MatricesShow relationships with scatter plots

• Slow perception

• May have interaction

• Does not scale well


2D Scatter Plot MatricesLet an algorithm choose the plots

16.07.2015 Fabian Keller 17[Zheng, 2014]

3D Scatter PlotsInteractive

• Only one additional dimension

• Expensive interaction, useless without!

• Limited benefit compared to 2D scatter plots


[Sedlmair, 2013]

Parallel Coordinate PlotDisplay >2 dimensions

16.07.2015 Fabian Keller 19Interaction Examples: https://syntagmatic.github.io/parallel-coordinates/

• Noisy• Slow perception• Meaning of x-axis?!

[Harvard Business Manager, 2015-07]

GlyphsEncode important information

• Memorable semantics

• Small

• Details through

interaction

• Overwhelming?


[Fuchs, 2013]

GlyphsDomain-specific clues


[Fuchs, 2014]

GlyphsTime series data

16.07.2015 Fabian Keller 22[Kintzel, 2011]

ConclusionHigh Dimensional Data Visualization

• Lots of DR / visualization techniques

• Even more combinations

• Application needs to be tailored to needs


“A problem well put is half-solved” – John Dewey

Thank you for your attention!

Questions?

Literature

• Sedlmair, Michael; Munzner, Tamara; Tory, Melanie (2013): Empirical guidance on scatterplot and dimension reduction technique choices.

• Zheng, Yunzhu; Suematsu, Haruka; Itoh, Takayuki; Fujimaki, Ryohei; Morinaga, Satoshi; Kawahara, Yoshinobu (2014): Scatterplot layout for high-dimensional data visualization.

• Card, S. K., Mackinlay, J. D., and Shneiderman, B., editors. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco. 1999.

• Fuchs, Johannes, et al. "Evaluation of alternative glyph designs for time series data in a small multiple setting." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2013.

• Christopher Kintzel, Johannes Fuchs, and Florian Mansmann. 2011. Monitoring large IP spaces with ClockView.

• Fuchs, Johaness et al. “Leaf Glyph Visualizing Multi-Dimensional Data with Environmental Cues“. 2014.

• Balasubramanian, Mukund, and Eric L. Schwartz. "The isomap algorithm and topologicalstability." Science 295.5552 (2002): 7-7.

• Roweis, Sam T.; Saul, Lawrence K. (2000): Nonlinear dimensionality reduction by locally linear embedding.

• dos Santos, S. and Brodlie, K. Gaining understanding of multivariate and multidimensional data through visualization. Computers & Graphics, 28(3):311–325. 2004.

• Harvard Business Manager, 2015-07: Andere Länder, anderer Stilhttp://www.harvardbusinessmanager.de/heft/d-135395625.html

• isomorphismes (2014). pca - making sense of principal component analysis, eigenvectors & eigenvalues - cross validated. http://stats.stackexchange.com/a/82427/80011


http://www.harvardbusinessmanager.de/heft/d-135395625.html

http://stats.stackexchange.com/a/82427/80011

Example Applications

• Biological / Medical (genes, fMRI)

• Finance (time series)

• Geological (climate, spatial, temporal)

• Big Data Analysis (Netflix Movie Rating Data)


Other DR techniquesMatlab toolbox for dimensionality reduction


• Principal Component Analysis (PCA)

• Probabilistic PCA• Factor Analysis (FA)• Classical multidimensional

scaling (MDS)• Sammon mapping• Linear Discriminant Analysis

(LDA)• Isomap• Landmark Isomap• Local Linear Embedding (LLE)• Laplacian Eigenmaps• Hessian LLE• Local Tangent Space

Alignment (LTSA)• Conformal Eigenmaps

(extension of LLE)

• Maximum Variance Unfolding (extension of LLE)

• Landmark MVU (LandmarkMVU)

• Fast Maximum Variance Unfolding (FastMVU)

• Kernel PCA• Generalized Discriminant

Analysis (GDA)• Diffusion maps• Neighborhood Preserving

Embedding (NPE)• Locality Preserving Projection

(LPP)• Linear Local Tangent Space

Alignment (LLTSA)• Stochastic Proximity

Embedding (SPE)

• Deep autoencoders (using denoising autoencoderpretraining)

• Local Linear Coordination (LLC)• Manifold charting• Coordinated Factor Analysis

(CFA)• Gaussian Process Latent

Variable Model (GPLVM)• Stochastic Neighbor

Embedding (SNE)• Symmetric SNE• t-Distributed Stochastic

Neighbor Embedding (t-SNE)• Neighborhood Components

Analysis (NCA)• Maximally Collapsing Metric

Learning (MCML)• Large-Margin Nearest Neighbor

(LMNN)

See: http://lvdmaaten.github.io/drtoolbox/

http://lvdmaaten.github.io/drtoolbox/

High Dimensional Data Visualization

Data & Analytics