Visualizing Data using t-SNE

Paper implementation of Visualisierung 2 course

Short Paper Summary#back to top

The t-SNE algorithm visualizes high-dimensional data by two- or three-dimensional points where as much of the significant structure of the original data is preserved in the low-dimensional representation. With this technique, similar objects in high-dimensional data space are represented by spatially close points and divergent data by distant points. So not only the local structure of the high-dimensional data is retained, but also the global structure which reveals possible clusters in the data. The basis of t-SNE is the Stochastic Neighbor Embedding (SNE) introduced by Hinton and Roweis but with an easier optimization approach and avoidance of crowding points together in the center.

Implementation Summary#back to top

The t-SNE algorithm is a functional implementation in F# in combination with the open source Aardvark platform developed by the VRVis Research Center. The Aardvark platform is a collection of algorithms and data structures for computer graphics and visualization with its own rendering engine. But the visualization is a simple interactive scatter plot embedded html-side done with Highcharts. The scatter plot can easily be saved as an image via the webside. The outcome of the implementation is a simple command line tool which takes the input file, a destination path for the resulting webside and an optional title for the plot, like for example: 
                                    tSNE -csv C:\tSNE\data.csv -o C:\tSNE\result.html -t pageTitle

Source Code#back to top

The program is written in F# using Visual Studio 2017. The whole solution can be found in src-folder of the submission. To compile the solution and download all necessary dlls you have to run the build.cmd first (simply double-click on it). Then you can open the Visual Studio solution in the src-folder.
Or you can start the prebuild command line tool by executing the tSNE.exe in the bin-folder of the submission.
The documentation of the source code can be found here.

How to run the cmd tool#back to top

The command line tool takes 3 input arguments: 
                                    tSNE -csv input_csv_file -o output_path_of_html [-t chart_title]
Where the -t parameter for the chart title is optional. If no title is given the program simply unses the filename. The output path has to be an (not yet existing) html-file.
The input file is a csv-file with the following layout: 
                                    class,val1,val2
                                    red,1,1
                                    red,2,2
                                    blue,3,3
                                    blue,4,4
Note: The first row contains the headers and the first header have to be "class"!
The resulting chart can be saved as an image via the website.

Results#back to top

Note: Click on the images to open the resulting interactive websites!

Akaze Features of Buildings

This dataset was provided by the VRVis Research Center and its representing labeled Akaze features from buildings. Akaze

MNIST dataset

The MNIST dataset represents handwritten digits from 1 to 10. Here a fewer amount of data samples was taken than in the paper. MNIST

COIL-20 dataset

The COIL-20 dataset are features of b/w images which represents different objects (like a piggybank). COIL-20

Leaf dataset

The Leaf dataset is describing leaves with 15 different features, like the asprect ratio or convexity. leaf