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GMU:Critical VR Lab I/L.-E. Kuehr (view source)
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[[File:VRI-LEK-Epochs.PNG|none|1000px|Learning Process]] | [[File:VRI-LEK-Epochs.PNG|none|1000px|Learning Process]] | ||
The resutling vector space is of very high dimensionality, thus cannot be easily visualized. To reduce the high dimensional space to three.dimensions the method ''t-distributed stochastic neighbor embedding'' is used, which keeps words close together that were close in the high dimensional space. | The resutling vector space is of very high dimensionality, thus cannot be easily visualized. To reduce the high dimensional space to three.dimensions the method ''t-distributed stochastic neighbor embedding'' is used, which keeps words close together that were close in the high dimensional space. | ||
[[File:VRI-LEK-Graph.png| | [[File:VRI-LEK-Graph.png|none|500px|Resulting Graph]] | ||
The resulting data is than imported into unity using a ''csv''-file and for every data-point a billboard-text of the word is generated. This process is repeated for every text. | The resulting data is than imported into unity using a ''csv''-file and for every data-point a billboard-text of the word is generated. This process is repeated for every text. | ||
[[File:VRI-LEK-5.png|none|400px]] | [[File:VRI-LEK-5.png|none|400px]] | ||